Appendix

513

A. Another example of a word bank

In Section 22.1, the mechanism of natural language communication is illustrated with an example of embedding a proposition into the knowledge representation of a hearer, and extracting this proposition when the hearer becomes a speaker. The format used turns out to be unsuitable, however, for automatically reading a content into and out of a database. As a solution, the data structure of a word bank is proposed and developed in Chapters 22-24. To strengthen the intuitive grasp of the word bank concept, the subcontext example 22.1.4 is translated here into an equivalent word bank.

A.l Embedding and extracting information

The example begins with the following representation of a simple subcontext:

A.1.1 SKETCH OF A SIMPLE SUBCONTEXT

FIDO

~ IS-A FRIENDS BROTHERS

DJG FEL(x).RITZ ZAC~DDIE This representation expresses that Fido is a dog, Felix and Fritz are his friends, and Zach and Eddie are his brothers.

In order to model natural language communication, a new piece of information is to be embedded (hearer mode) into the subcontext A.1.1.

A.1.2 ADDING THE CONTENT OF Fido likes Zach TO A.1.1

LIKE

~ AGENT PATIENT . . FIDO

~ IS-A FRIENDS BROTHERS .

DJG FEL(x).RITZ EDD~Ac·~

516 A. Another example of a word bank

When the hearer turns into a speaker, the piece of information Fido likes Zach must be extracted from the subcontext. This requires copying the information in question and mapping it into natural language.

A.2 Translating the content of a knowledge base into propositions

The first step of representing the content of A.l.l within the data structure of a word bank is to translate the original hierarchy into a sequence of elementary propositions:

A.2.1 PROPOSITIONAL PRESENTATION OF SUBCONTEXT A.1.2

1. Fido is a dog. 2. Fido has friends. 3. The friends are Zach and Eddie. 4. Fido has brothers. 5. The brothers are Felix and Fritz. 6. Fido likes Zach.

The propositions 1-5 in A.2.1 correspond to the content of the initial subcontext A.l.l, while proposition 6 corresponds to the subcontext extension illustrated in A.l.2.

A.3 An equivalent graphical representation

Using the graphical style of 3.4.2, the above sequence of elementary propositions may be represented as follows:

A.3.1 GRAPHICAL PRESENTATION OF THE PROPOSITIONS IN A.2.1

be ~

Flido dog have ~

Fido friend

lA friend

have ~

Fido brother

Zach, Eddie

lA brother

like ~

Fido Zach

Felix, Fritz

A.4 Word bank representation 517

This representation as a sequence of concatenated elementary propositions shows that the word bank approach is conceptually completely different from the intuitive hier­archy (cf. A.l.l) and its model-theoretic (cf. 22.2.1) or frame-theoretic (cf. 22.2.2) realizations.

Compared with the intuitive hierarchy A.l.l, the graphical representation A.3.1 con­sists of complete propositions, whereby the verbs establish intrapropositional relations between subjects and objects. The vertical lines indicate coreference between nouns and illustrate the id-type of extrapropositional concatenation.

A.4 Word bank representation

From the graphical presentation A.3.1 it is only a small step to the corresponding word bank. While A.3.1 expresses intrapropositional relations in terms of the verbal connecting lines, A.4.1 joins proplets into propositions by means of common prn values. Furthermore, the extrapropositional sequencing of A.2.1 is expressed in A.4.1 in terms of suitable cnj values.

A.4.1 SUBCONTEXT A.l.l AS A WORD BANK

TYPES PROPLETS

[fooo Ml [funo M l f~o M I [fuoc'M I ARGl: ARGl: Fido ARGl: friend ARGl: brother

ARG2: ARG2: dog ARG2: Zach, Eddie ARG2: Felix, Fritz cnj: 2 and 3 cnj: 4 and 5 cnj: cnj: 1 and 2 3 and4 5 and6 prn: prn: 1 prn: 3 prn: 5

[ mo bro~'] [ Mg' bro~' l [Mg' brot~'] FUNC: FUNC: have FUNC: be id: id: id: prn: prn: 4 prn: 5

[~ OOgl [~OOg l FUNC: FUNC: be id: id: prn: prn: 4

[~E~'] [~Eddkl FUNC: FUNC: be id: id: 3 prn: prn: 3

[ ~ F,lul [~&IU l FUNC: FUNC: be id: id: 4 prn: prn: 5

518 A. Another example of a word bank

[arg: Fritz] FUNC: id: pm:

[arg: Fidol FUNC: id: pm:

[arg: friend] FUNC: be id: 1 pm: 1

rfunc: have1 ARGl: ARG2: cnj: pm:

rfunc: like1 ARGl: ARG2: cnj: pm:

[arg: Zachl FUNC: id: pm:

[arg: Fritz l FUNC: be id: 5 pm:5

[arg: Fido l FUNC: be id: 1 pm: 1

[arg: friend l FUNC: have id: pm:2

[arg: Fido l FUNC: have id: 1 pm:2

[arg: Fido l [arg: Fido l ~UNC: have ~UNC: like & ld: 1 ld: 1 pm: 4 pm: 6

[arg: friend] FUNC: be id: pm:3

func: have ARGl: Fide ARG2: friend cnj: 1 and 2

1-conceptzoc: x17 argl: Fide

2 and 3 pm:2

rfunc: like 1 ARG1: Fide ARG2: Zach & cnj: 5 and 6 prn: 6

arg2: brother cnj: 3 and 4

4 and 5 pm:4

[arg: Zach l [arg: Zach l ~UNC: be ~UNC: like & ld: 2 ld: 2 prn: 3 prn: 6

The prn values in A.4.1 agree with the proposition numbers in A.2.1. The id values of proper names correspond to their order of appearance. In other nominal proplets, the id values are left unspecified: they may either be added later in accordance with the content of the text, or reconstructed in terms of inferences. The ordering of the propositions is expressed by the cnj values of verbal proplets, e.g., cnj: 5 and 6.

A.S Embedding and extracting propositional content

Consider now the reading-in of proposition 6 into a state of the above word bank without the prop lets marked with ' & '. The semantic representation of proposition 6 is the following small word bank:

A.5.1 SEMANTIC REPRESENTATION OF PROPOSITION 6

TYPES PROPLETS

[arg: Fidol FUNC: id: pm:

[arg: Fido l FUNC: like id: ? pm:6

[

func: like] ARGl: ARG2: cnj: pm:

[arg: Zachl FUNC: id: pm:

~unc: like l ARGl: Fido ARG2: Zach nj:? m:6

[arg: Zach l FUNC: like id:? pm: 6

A.5 Embedding and extracting propositional content 519

The automatic translation of English sentences into corresponding sets of proplets is handled by a semantically interpreted LA-grammar of English (cf. LA-INPUT defined in 24.1.7).

The pragmatic interpretation of this semantic representation consists in sorting the proplets of the small word bank into the large word bank representing the context of use. This embedding procedure requires (i) adding the new proplets in the correspond­ing token line (as shown in A.4.1) and (ii) assigning the correct cnj and id values by means of counters and inferences.

Reading a propositional content out of a word bank is as simple as reading it in. It is based on navigating through the proplets of the word bank, whereby a special type of LA-grammar computes the possible continuations (cf. LA-MOTOR defined in 23.2.2). The proplets traversed are matched by language proplets (two-level nav­igation, cf. Section 23.2). They form a sequence which is input to an LA-grammar for producing the correct language-dependent surface (cf. LA-OUTPUT defined in 24.2.6).

B. Interpretation of a complex sentence (IA-E4)

The analysis of English in Chapter 17 describes different syntactic constructions in terms of the formal LA-grammars LA-E2 and LA-E3. What is missing, however, is a semantic interpretation.

The fragment of English presented in Sections 24.1-24.3 is more or less complete in the functional sense, because it illustrates natural language interpretation, concep­tualization, and production. Compared to the constructions handled in Chapter 17, however, the sentences on which the fragment is based are very simple.

This appendix presents the syntactic-semantic derivation of an English sentence which is more complex than the one illustrated in Section 24.1 ( cf. 24.1.8 -24.1 0). Each step of the syntactic-semantic derivation is represented explicitly in the format of 24.1.5.

B.l The sample sentence

The sentence to be analyzed is the following:

B .1.1 The man gave Mary a flower because he loves her.

Syntactically, the derivation is based on an extension of LA-E3 (cf. 17.5.5), called LA­E4 and defined below. The extension is necessary because sentence B.1.1 contains an adverbial subclause, a construction not handled by LA-E3.

The result of the derivation is a set of prop lets ( cf. B .12.1) representing the propo­sitions give man Mary flower and love man Mary. The two propositions are concate­nated by the conjunction because.

The corresponding production of B .1.1 would be based on a subordinating conjunc­tion-based forward navigation (cf. 23.1.2), driven by LA-MOTOR and realized by an extension of LA-OUTPUT (cf. 24.6.2).

B.2 Definition of LA-E4

The definition of LA-E4 uses the format of rule alternatives (clauses) as presented in 18.4.4.

522 B. Interpretation of a complex sentence (LA-E4)

B.2.1 lA-E4 FOR ADVERBIAL SUBCLAUSES OF ENGLISH

LX= LX of IA-E3 plus {(slowly (ADP) *),(because(# ADP) *)} Variable definitions= those of IA-E3 plus mn E {np U {V, VI}}

STs =def{ [(x) { 1DET+ADJ,2DET+N,3NOM+FV,4AUX+MAIN,5STRT-SBCL}]}

DET +ADJ: (n x) (ADJ) DET +N: (n x) (n)

NOM+FV: (np # x) (np' y V) (np) (np1 x V)

FV+MAIN: (np' # x) (y np) (np' x # y) (z np)

(np' x V) (y np) AUX+NFV: (aux # x V) (aux)

(aux # x V) (y aux) (aux V) (x aux)

AUX+MAIN:(np aux V) (x np') ADD-ADP: (x ADP) (mn y)

(mn y) (x ADP) STRT-SBCL: (# x) (y np)

::::} (n x) { 6 DET+ADJ, 7 DET+N}

::::} (x) { 8 NOM+FV, 9 FV+MAIN, 10 AUX+NFV,

11 ADD-ADP, 12 IP} ::::} (y # x)

::::} (x V) { 13 FV+MAIN, 14 AUX+NFV, 15 ADD-ADP,

16 IP}

::::} (y x) ::::} (z x # y) ::::} (y XV){ 17 DET+ADJ, 18 DET+N, 19 FV+MAIN, 20 IP}

::::} (x V) ::::} (y # X V) ::::} (x V) { 21 FV+MAIN, 22 IP}

::::} (x aux VI) {23 AUX+NFV, 24 DET+ADJ, 25 DET+N}

::::} (x mn y) ::::} (X mn y) { 26 STRT-SBCL, 27 NOM+FV, 28 FV +MAIN}

::::} (y np # x) {29 DET+ADJ, 30 DET+N, 31 NOM+FV,

32ADD-ADP}

IP: ( vt) ( vt x) ::::} (x) {}

STF =def { [(V) rpip]. [(VI) rpip]}

For simplicity, the semantic operations associated with each clause are specified only in the following examples of rule applications.

The incoming next words are analyzed in terms of lexical lookup. The following feature structures are like those in 24.1.3, except that their sem-features are more differentiated for the purpose of the derivation at hand.

B.2.2 NOMINAL, VERBAL, AND ADJECTIVAL PROPLET STRUCTURES

nominal proplet verbal proplet adjectival proplet

sur: sur: syn: sur: syn:

syn: properties: properties:

rDR] rDR] [~" l cont.: VERB: cont.: NP: NP: ] sem: cont.: VERB: sem: sem:

;dent' ~-1 "d pm:J . pm: 1 ent.: id: I dent.: cnj:]

mod: arg: func:

B.3 Pre-verbal application of DET +N 523

The sem feature of nominal proplets specifies the continuation attributes MOOR (modifier) for adnominals and VERB for the functor of the elementary proposition of which it is a part - as well as characteristic properties such as number. The identifier feature contains the attributes prn and id. In elementary propositions consisting of several, possibly equal, proplets, these may be distinguished in terms of different id values.

The sem feature of verbal proplets specifies the continuation attributes MOOR for adverbials and N P for the valency fillers - as well as properties such as tense and mood. The identifier feature contains the attributes prn and cnj. The cnj-attribute specifies extrapropositional relations based on conjunctions, e.g., [cnj: 1 because 2] (cf. B.8.1).

The sem feature of adjectival proplets specifies the continuation attributes NP for adjectival use and VERB for adverbial use- as well as properties such as degree.

In the following, the features properties, cont(inuation), and identifier are abbre­viated as P, C, and I, respectively, for reasons of space. In accordance with PoP-7 ( cf. 6.1. 7), the content unit of an arg-attribute may be a symbol, an index, or a name; the content unit of a modr-attribute may be a symbol or an index; and the content unit of a tunc-attribute may be a symbol only.

B.3 Pre-verbal application ofDET+N

The derivation of example B.l.l begins with an application of DET +N:

B.3.1 APPLYING DET+N TO the+ man

syn: (n x) sem:

sur: the syn: (SN' SNP)

sem:

P: (sg def)

C· [MODR:] . VERB:

I: [?rn: (1)] 1d: +l

arg: CD

(n)

sur: man syn: (SN)

P:

===> (x) nw.arg -EJ-+ ss.CD COPYss

sur: the man syn: * (SNP)

P: (sg def)

C. [MODR:] + . VERB: ===> C· [MODR:] . VERB:

sem: . [prn: =]

I. id: = sem:

1: [?rn: (1)] 1d: * 1

arg: man 2 arg: *man

The semantic operation of DET +N replaces the variable CD in the sentence start ss by the value of the feature arg in the next word nw (written as nw.arg --0--+ ss.CD). Then the proplet of the ss, but not of the nw, is included in the resulting ss (written as copy88 ). The proposition number is incremented by the grammatical control structure at the beginning of a new sentence (prn: ( 1) ). The incrementation of the id value in the result proplet is lexically-based (id: + 1).

524 B. Interpretation of a complex sentence (LA-E4)

The result ofB.3.1 represents a singular definite noun phrase with the content unit man. Thus, noun phrases consisting of a determiner and a noun are built into one proplet by the semantics. This is done by copying the relevant values of the noun (content word) into the proplet of the determiner (function word).

An analogous procedure applies to complex verb phrases, e.g., has seen - with has as the function word (cf. 17.3.2). In contrast, the phrasal parts of a complex noun or verb phrase, such as relative or adverbial clauses (cf. 23.1.2), are analyzed as elementary propositions which are concatenated with the higher sentence via their id or cnj value.

B.4 Application of NOM+FV

The next composition is based on the second clause of the rule NOM+FV.

B.4.1 APPLYING NOM+FV TO the man+ gave

syn: (np) sem:

sur: the man syn: (SNP)

sem:

P: (sg def)

C: [~~~:] I. [pm: (1)] . id: 1

arg: man

+

(np' x V)

sur: gave syn: (N' D' A' V)

P: past tense

C: [~DR:] sem:

1: [~:i =] func: give

sur: syn: (SNP)

P: (sg def)

===:}

nw.func ---0-+ ss.VERB ss.arg -E}-t nw.NP COPYss COPYnw

3

sur: the man gave syn: * (D' A' V)

P: (past tense)

[MODR: ] ===:} C: VERB:* give [MODR: ]

C: NP: * (man) sem:

1: [pn;t: * (1)] CnJ:

sem: I . [pm: (1)] . id: 1

arg: man func: give

(x V)

3

The semantic operations specify that the content unit of the ss is to be copied into the continuation attribute of the nw and vice versa (written as ss.arg -@-+ nw.NP and nw.func-@-+ ss.VERB, respectively). The proplets of the ss and the nw are all included in the result (written as COPYss and COPYnw• respectively). Note that only one proplet in the resulting ss has a non-NIL sur value (cf. 24.1.5).

B.5 Application ofFV+MAIN 525

B.S Application of FV +MAIN

The next composition is based on the third clause of the rule FV +MAIN.

B.5.1 APPLYING FV+MAIN TO the man gave+ Mary

syn: (np' x V) sem:

sur: the man gave syn: * (D' A' V)

P: (past tense)

[MODR: ] C: NP: (man)

sem: 1: [pn:: (1)]

Cnj:

func: give

+

3

(y np) ==> nw.arg ---E}--t ss.NP ss.func ---EJ-+ nw.VERB COPYss COPYnw

sur: Mary syn: (SNP)

sem:

P: (sg name)

C· [MODR:] . VERB:

[pm: =] I: id: + 1

arg: Mary 4

(y x V)

sur: the man gave Mary syn: *(A' V)

sur: syn: (SNP)

P: (past tense)

C: [~~~~~n. * Mary)] sem:

1: [pn:: (1)] Cnj:

func: give 3

P: (sg name)

[MODR: ] C: VERB: * give

sem: I: [~m: * (1)]

ld: *2 arg: Mary

The semantic operation nw.arg --@--+ ss.NP in B.5.1 illustrates additive copying (cf. 24.1.6, 3) of a value: even though the NP-attribute of give already has a value (namely man), Mary is added as an additional filler. The incrementation of the identity number in the prop let Mary is lexically-based (id + 1 ).

For reasons of space, the following rule applications will not explicitly list proplets which are not modified any further (here the proplet of the man). These proplets are presupposed implicitly, however, because they are needed in the pragmatic embedding of the semantic representation into the word bank.

B.6 Reapplication of FV +MAIN

The next composition is based on a reapplication of the third clause of FV+MAIN. Even though the nw is a proper name in B.5.1 and a determiner in B.6.1, the rule application is based in both instances on the same syntactic and semantic patterns.

526 B. Interpretation of a complex sentence (IA-E4)

B.6.1 APPLYING FV+MAIN TO the man gave Mary+ a

syn: (np' x V) sem:

(y np) ====* (y x V) nw.arg ---@-+ ss.NP ss.func ---EJ-+ nw. VERB copy •• COPYnw

sur: the man gave Mary syn: (A' V)

sur: a syn: (SN' SNP)

sem:

P: (past tense) P: (sg indef)

C: [~(~~n. Mary)] + sem:

C. [MODR:] . VERB:

I: [pll_l: (1)] Cnj:

func: give

[pm: =] I: id: +1

3 arg: (j)

sur: the man gave Mary a syn: * (SN' V)

P: (past tense)

[MODR: ] C: NP: (man, Mary, *<D)

sem: I: [pll_l: (1)]

CnJ: func: give

5

sur: syn: (SN' SNP)

P: (sg indef)

C: [~~~:*give] sem:

I: [pm: (1)] 1d: * 3

3 arg: (j)

5

Because the nw is a determiner, its arg-attribute has the variable <D as its content unit value. This value is copied as a further valency filler into the NP-attribute of give.

B.7 Post-verbal application ofDET+N

The next composition illustrates a post-verbal application of DET+N (cf. B.3.1).

B.7.1 APPLYING DET+N TO The man gave Mary a+ flower

syn: (nx) sem:

sur: the man gave Mary a syn: (SN' V)

P: (past tense)

C: [~(~~n. Mary, <D)] sem:

I: [pll_l: (1)] CnJ:

func: give 3

sur: syn: (SN' SNP)

sem:

P: (sg indef)

[MODR: ] C: VERB: give

I. [pm: (1)] . id: 3

arg: (j) 5

+

(n) ====* (x) nw.arg---@-+ ss.<D copy ••

sur: flower syn: (SN)

P:

C: [~~~:] sem:

. [pm: =] I. id: = arg:jiower

6

B.8 Transition to the subordinate clause based on ADD-ADP 527

sur: the man gave Mary a flower syn: (V)

P: past tense

C: [~~~~~n, Mary, * flower)] sem:

I: [pn:: ( 1 >] Cnj:

func: give 3

sur: syn: (SNP)

sem:

P: (sg indef)

C: [~~~::give] I. [prn: (1)] . id: 3

arg: * flower

The operation nw.arg --8-+ ss.CD replaces all occurrences of the variable CD- placed in the previous rule application - simultaneously by flower. Again, the pre- and postverbal application of this rule is based on the same syntactic and semantic pat­terns.

B.S Transition to the subordinate clause based on ADD-ADP

The last rule application of DET+N could now be followed by IP (cf. B.2.1), con­cluding the sentence. The surface, however, continues with the conjunction because, which is matched by the nw-pattem of ADD-ADP.

B.8.1 APPLYING ADD-ADP TO The man gave Mary afiower +because

syn: (mn y) sem:

sur: the man gave Mary a flower syn: (V)

P: (past tense)

C: [~~~~~n, Mary, flower)] sem:

I: [pn:: (1)] CnJ:

func: give

+

3

(x ADP)

sur: because syn: (# ADV)

P:

C: [~~DR:]

===? (x mn y) ss.prn -EJ-+ nw.p1 nw.prn -EJ-+ nw.p2 nw.cnj -EJ-+ ss.cnj COPYss COPYnw

[prn: EB l

I· p1: · cnj: [~~~:because]

sem:

func: 7

528 B. Interpretation of a complex sentence (IA-E4)

sur: syn: (V)

P: (past tense)

[MODR: ] C: NP: (man, Mary, flower)

[prn: (1) l

I· pl: 1 · cnj: * [;~~~because]

sem:

func: give 3

sur: the ... flower because syn: * (# V)

sem:

P:

C: [~DR:]

I· p1: * 1 [prn: * (2, 1) l

· cnj: [con: because] p2: *2

func: 7

Controlled by the lexical analysis of because, the proposition number of the sub­clause is incremented ([prn: EB]) and added in front of the previous proposition num­ber (prn: (2, 1) ). Also, at the beginning of a new elementary proposition, the nominal identity number is automatically set back to 0 by the grammatical control structure.

The conjunction because introduces the following cnj feature structure:

Intuitively this may be read as p1 because p2, whereby the attributes p1 and p2 take proposition numbers as their values. The semantic operations in B.8.1 first provide values for pl and p2 (ss.prn --E}---t nw.p1 and nw.prn --E}---t nw.p2) and then copy the completed cnj-attribute into the verb-proplet of the main clause (nw.cnj ---@-+ ss.cnj). For simplicity, this complex cnj value will be written as [cnj: 1 bee 2].

B.9 Beginning of the subordinate clause based on START-SUBCL

Following the subordinating conjunction because, the subclause continues with the nominative. This argument proplet happens to take an index as the content unit of its arg -attribute.

B.9.1 APPLYING START-SUBCL TO The man gave Mary ajlower because+ he

syn: (# x) sem:

sur: the man gave M. a f. because syn: (# V)

sem:

P:

C: [~~DR:] I· [prn: (2, 1) ] · cnj: 1 bee 2

func: 7

(ynp) ===*

+

sur: he syn: (SNP)

P: (nom sg)

nw.arg ---0--+ ss.NP COPYss COPYnw

sem:

C. [MODR:] . VERB:

[prn: =] I: id: +1

arg: pro-1 8

(y np # x)

B.10 Reapplication ofNOM+FV 529

sur: the man gave M. a f. because he syn: * (SNP # V)

sem:

P:

C: [~~~~:ro-1] I· [pm: (2, 1) ] · cnj: 1 bee 2

func: 7

sur: syn: (SNP)

sem:

P: (nom sg)

C' [MODR:] . VERB:

I: [~m: * (2, 1)] Id: * 1

arg: pro-] 8

The semantic operation nw.arg --{D--+ ss.NP copies the content unit ofthe next word into the continuation predicate of because. Controlled by the lexical analysis of he, the identity number of the first subclause filler is incremented to '1'.

B.lO Reapplication of NOM+FV

The verb of the subordinate clause is added by the first clause of NOM+FV. The semantic interpretation copies all the relevant values of the verb into corresponding attributes of the conjunction proplet because, which has been functioning as a place­holder of the verb in that it already contains the argument value of the nominative. This principle of using the subordinating conjunction for storing valency fillers works also for German, where subordinate clauses have the verb in clause-final position (see Hausser 2000, pp.494-497 for an explicit derivation).

B.lO.l APPLICATION OF NOM+FV TO The man g. M. af because he+ loves

syn: (np # x) sem:

sur: The ... because he syn: (SNP # V)

P:

[MODR: ] C: NP: pro-1

sem: I· [pm: (2, 1) ] · cnj: 1 bee 2

func: 7

sur: syn: (SNP)

sem:

P: (nom sg)

C' [MODR:] . VERB:

I. [pm: (2, 1)] 0 id: 1

arg: pro-]

+

8

sur: The ... because he loves syn: ( * A' # V)

sem:

P: *present tense

C: [~~~~-1] I· [pm: (2, 1) ] · cnj: 1 bee 2

func: *love

(np' y V) ===} (y # x) nw.func ---EJ-+ ss.func nw.P ---EJ-+ ss.P nw.func ---EJ-+ ss.VERB COPYss

sur: loves syn: (S3' A' V)

P: (present tense)

C: [~~DR:] sem:

. [pm: =] I. 0

CnJ: func: love

9

sur: syn: (SNP)

P: (nom sg)

[MODR: ] C: VERB:* love

sem:

7

I . [pm: (2, 1)] 0 id: 1

arg: pro-] 8

530 B. Interpretation of a complex sentence (IA-E4)

The semantic operation nw.func --EJ-+ ss.func copies the content unit of the nw into the one func-attribute of the ss which is still empty, i.e., the proplet which originated from because. Similarly, the grammatical properties of the nw are copied into the corresponding attribute of the ss (nw.P --EJ-+ ss.P). Then the content unit of the nw is copied exclusively into the empty VERB-attribute of the ss, i.e., the proplet of he (nw.func --EJ-+ ss.VERB). All relevant properties of the nw love have now been copied into the ss, which is why only the proplets of the ss are taken into the result (COPYss).

B.ll Completing the subordinate clause with FV +MAIN

The last combination in the derivation of B.l.l adds the object noun phrase of the subordinate clause using the first rule alternative ofFV+MAIN (cf. B.2.1).

B.ll.l APPLICATION OF FV +MAIN TO The m. g. M. a f because he loves + her

syn: (np' # x) sem:

(y np) ==> (y x)

sur: The ... because he loves syn: (A' #V)

sem:

P: present tense

C: [~~~-1] I· [pm: (2, l) ] · cnj: 1 bee 2

func: love 7

+

sur: her syn: (SNP)

sem:

P: {obl sg)

C. [MODR:] . VERB:

[pm: =] I: id: +1

arg:pro-2

sur: The ... because he loves her syn: { * V)

sem:

P: present tense

C: [~~~-1 * pro-2 ]

I· [pm: * {2-, 1)] · cnj: 1 bee 2

func: love 7

10

nw.arg --8-* ss.NP ss.func -EJ-+ nw. VERB ss.pm ~ ss.pm copy •• COPYnw

sur: syn: (SNP)

P: {obl sg)

[MODR: ] C: VERB:* love

sem: I: [rm: * {2-, 1)]

ld: *2 arg: pro-2

10

In addition to the usual cross-copying between the verb and the new noun phrase, the semantic operations of this rule contain ss.prn --8-+ ss.prn, which serves to deac­tivate the proposition number of the embedded clause after its completion ( cf. 24.1.6, 6). The purpose of this operation is especially apparent in embedded subclauses.

B.12 Resultofthederivation 531

B.11.2 PROPOSITION NUMBER OF AN EMBEDDED SUBCLAUSE

the man, pm: ( 1) because he loves Mary

pm: (2, 1)

gave her a flower. pm: (2-, 1)

The proplets of the subclause require the proposition number 2, yet after the com­pletion of the subclause the proplets of the main clause remainder must have the

earlier proposition number 1. For this purpose the number of the new, embedded proposition is written before the number of the old proposition (thus remembering rather than overwriting the old proposition number). When the embedded proposition is completed, the semantic operation ss.prn --8--+ ss.prn deactivates the first un­marked element of the prn value (here (2-, 1) ). In subsequent rule applications, the first non-deactivated (i.e., unmarked) proposition number is used. This method based on a simple stack is suitable to provide the correct proposition number in embedding constructions of arbitrary depth (e.g., 9.2.1).

B.12 Result of the derivation

The left-associative syntactic-semantic derivation of example B.1.1 results in an un­ordered set of coindexed, bidirectional proplets which is summarized below.

B .12.1 SLIM SEMANTIC REPRESENTATION OF EXAMPLE B .1.1

the man sur: syn: (SNP)

P: (sg def)

[MODR: ] C: VERB: give

sem: I. [pm: (1)] . id: 1

Mary sur:

arg: man

syn: (SNP)

sem:

P: (sg name)

C: [~~~B~:give] I. [pm: (1)] . id: 2

arg: Mary 4

gave sur: syn: (V)

sem:

a flower sur:

P: (past tense)

C: [~~~~~n, Mary, flower)]

I· [pm: (1) ] · cnj: 1 bee 2

func: give

syn: (SNP)

sem:

P: (sg indef)

C: [~~~B~:give] I. [pm: (1)] . id: 3

arg:jlower

3

532 B. Interpretation of a complex sentence (IA-E4)

because loves sur: syn: (V)

sem:

P: (3sg, present tense)

C: [~~~-1 , pro-2] I· [pm: (2-,1)] · cnj: 1 bee 2

func: love

he sur: syn: (SNP)

sem:

7

P: (nom sg)

[MODR: ] C: VERB: love

I. [pm: (2-, 1)] 0 id: 1

arg: pro-1

her sur: syn: (SNP)

sem:

8

P: (obl sg)

[MODR: ] C: VERB: love

I. [pm: (2-,1)] 0 id:2

arg:pro-2 10

The numbers 1, 3, 4, 5, 7, 8, and 10 subscripted to the L-proplets correspond to the word numbers of the input sentence The1 man gave3 Mary4 a5 flower because7

he8 loves her10 .

There are no L-proplets with the numbers 2, 6, and 10 because these proplets were discarded after copying their content units into the related function word proplets. More specifically, man and flower were copied into the preceding determiner proplets, while loves was copied into the proplet of the conjunction because beginning the subordinate clause.

For storing this semantic interpretation in a word bank, only the sem-features of the L-proplets are used. In reality, the identity of he and her with the man and Mary, re­spectively, expressed by the respective id-values, would be decided only after reading the sem-features into the word bank, based on the application of inferences.

C. Subordinating navigation in the speaker mode

In Section 23.2, the navigation through a word bank is illustrated with only the most basic extrapropositional continuations based on the identity of arguments (cf. 23.2.3) and the conjunction of functors (cf. 23.2.4). These navigation types are realized in natural language as a sequence of main clauses (cf. 24.2.1-24.2.8). Other navigation types for handling subordinate clauses are presented below.

C.l Different navigation types

The id-and cnj-continuations permit a coordinating (paratactic) traversal of elemen­tary propositions which is realized in language as a sequence of main clauses. For example, the two concatenated elementary propositions 1 and 2 of the subcontext 24.1.11 allow a temporal and an antitemporal cnj-navigation corresponding to the following sentences:

C.1.1 CNJ-CONCATENATION

Peter leaves the house. Then he crosses the street. Peter crosses the street. Before that he leaves the house.

The subcontext 24.1.11 permits also coordinating id-navigations such as the follow­ing:

C.1.2 ID-CONCATENATION

Peter orders a salad. The salad is eaten by Peter.

In addition to the coordinating concatenation of elementary propositions, the natural languages permit subordinating (hypotactic) embedding. One universal prototype is adverbial clauses. It is based on an cnj-continuation.

C.1.3 CNJ-SUBORDINATION (ADVERBIAL CLAUSES)

Before Peter crosses the street, he leaves the house. Peter, before he crosses the street, leaves the house.

534 C. Subordinating navigation in the speaker mode

Peter leaves the house, before he crosses the street. After Peter leaves the house, he crosses the street. Peter, after he leaves the house, crosses the street. Peter crosses the street, after he leaves the house.

The other universal prototype is relative clauses. They are based on an id-continuation.

C.1.4 ID-SUBORDINATION (RELATIVE CLAUSE)

Peter, who leaves the house, crosses the street.

The surfaces in C.l.1, C.l.3, and C.l.4 are based on the same propositional content. The differences in the surfaces are direct reflections of different navigations.1

C.2 Embedding constructions

In the embedding constructions C.l.3 and C.l.4, traversal of a new proposition is initiated before the current elementary proposition has been completely traversed. The formal handling of these embedding navigations is based on a slight modification of the tracking principles 23.3.3. The modification consists in placing return markers T in the word bank. The return marker indicates where the current proposition has been left for an embedded one.

Each time a return marker has been placed, the tracking principle of completeness is transferred from the current proposition to the embedded proposition. This procedure is recursive such that each embedding may be followed by another one. As soon, however, as an embedded proposition has been traversed completely, the navigation returns automatically to the return marker placed last and removes it. Thereby the tracking principle of completeness is automatically reactivated for the next higher proposition. As soon as that proposition has been traversed completely, the procedure is repeated until the highest elementary proposition is reached again.

Consider, for example, an id-navigation underlying the beginning of a relative clause. The navigation rule A+id=F is part of LA-MOTOR as defined in 23.2.2.

C.2.1 APPLYING A+ID=F IN THE WORD BANK 24.1.11

[arg: a l . FUNC:b

A+1d=F: id: m

prn: k

[arg: a l ~UNC: c ===}

1d: m prn: l

[arg: Peter l FUNC: cross id: 1 prn: 2

[arg: Peter l FUNC: leave id: 1 prn: 1

[func: c ] A~G:xay CnJ: prn: l

{ 7F+A=F,} 8F+A=A

~unc: leave l ARG: Peter house nj: [1 2] rn: 1

C.3 Realization of clauses with the verb in final position 535

A corresponding navigation traversing the propositions 2 and 1 of the word bank 24.1.11 is shown in C.2.2, which indicates prn- and id-values of the proplets, the return marker T, and its removal - represented as .A..

C.2.2 ADNOMINAL EMBEDDING NAVIGATION (PREVERBAL)

Peter, who leaves the house, crosses the street.

'f' cross prn:2

Peter prn:2 id: 1

A+id=F: leave house ..6. prn:l prn:l

id:3

street prn: 2 id: 2

The return marker is placed by the navigation rule F+A=A (cf. 23.2.2) at the functor proplet because there the nominal continuations - needed after the return into the current clause- are specified. F+A=A calls the rule A+id=F. The output of A+id=F is the functor proplet specified by the second nominal input proplet. In this way the nominal embedding may be continued immediately with the rule F+A=F.

C.3 Realization of clauses with the verb in final position

The language-specific word order corresponding to universal navigation illustrated above is controlled by a language-specific LA-OUTPUT (defined in 24.2.6 for En­glish). Such an LA-grammar realizes the clause-final position of the verb in subordi­nate clauses of German as follows:

C.3.1 WORD ORDER OF ADNOMINAL EMBEDDING IN GERMAN

Peter, der das Haus verlassen hat, uberquert die StraBe. Peter, who the house left-has, crosses the street.

realization F+A=A t buffer: [Verb]+ [NP1l ===* [Verb]'f' [NPI]

realization A+id=F t buffer: *[NP1] [Verb] 'f' + [NP1'l ===* *[NPI] PRO [Verb]'f' [Verb']

realization F+A=F 2t It buffer: *[NP1] *[PRO] [Verb]'f' [Verb']+ [NP2'l ===* *[NPI] *[PRO] [Verb]..&. [Verb'] [NP2']

F+A=F realization buffer: *[NPI] *[PRO] *[NP/] *[Verb'] [Verb]..&.+ [NP2] 1 t 2t

===* *[NPI] *[PRO] *[NP2'] *[Verb'] [Verb]..&. [NP2] 1 Within the SLIM theory of language the phenomena of topic-comment (or theme-rheme) structure

in combination with word order, choice of verbal mood, of pronouns, of hypo- versus parataxis, etc., are all treated uniformly as linguistic reflections of particular types of navigation for particular communicative purposes.

536 C. Subordinating navigation in the speaker mode

Proplets of the subordinate clause are marked with 1• The buffer contains the L­proplets matching the navigation (cf. 24.2.4). L-proplets in the buffer already realized by LA-OUTPUT are marked with '*'.

The navigation based on the rule A+id=F goes from two coreferential input nomi­nals directly to the verb2 of the second nominal. Therefore, the relative pronoun PRO is contributed to the buffer not by the universal navigation, but rather by the language­specific LA-OUTPUT for German.

As an example of an adverbial embedding, consider the cnj-navigation underlying the beginning of an adverbial clause:

C.3.2 APPLICATION OF F+CNJ=F IN THE WORD BANK 24.1.11

[func: a l . ARG:x

F+cnJ=F: cnj: m C n pm:n

...

ARG:y [func: b l cnj: m C n ===}

pm:m

[func: bl

9F+A=F, { lOF+A=A}

r~~[:;;J:er street1 Cnj: 1 2

pm:2

[func: leave l ARG: Peter house cnj: [12] pm: 1

~unc: leave l ARG: Peter house nj: [12] m: 1

This application of F+cnj=F navigates from the functor cross (START) extraproposi­tionally to the functor leave (NEXT) which results as the NEW START. The return marker T is added to the first functor proplet.

C.4 Lexical realization of conjunctions

In cnj-navigations, the conjunction (in its universal, language-independent form) is contained in the cnj-feature of the functor proplet. In natural languages it has differ­ent realizations depending on whether the extrapropositional navigation is forward or backward, and on whether it is coordinating (paratactic) or subordinating (hypotactic ).

C.4.1 DIFFERENT REALIZATIONS OF CONJUNCTIONS

coordinating forward: coordinating backward:

subordinating forward: subordinating backward:

temporal pl. Thenp2. p2. Earlier pl.

p1, before p2, pl. p2, after p1, p2.

causal pl. Therefore p2.

p1, for which reason p2, pl. p2, because p1, p2.

modal pl. Thus p2.

p1, as p2, p1

2 Note that the SOV language Korean does not have relative pronouns. In this case, the above sentence transliterates as The house leaves Peter the street crosses or Peter the house leaves the street crosses.

C.5 Multiple center embeddings 537

In C.3.2, the conjunction is realized as after, due to (i) the direction and (ii) the hy­potactic nature of the navigation.

Once the embedded proposition has been traversed completely using F+A=F, the navigation returns into the next higher proposition to traverse the rest of its proplets. This is shown schematically in C.4.2, analogous to C.2.2.

C.4.2 ADVERBIAL EMBEDDING NAVIGATION

Peter, after he left the house, crossed the street.

.,cross pm:2 (2 then 3) (1 then 2)

Peter pm:2 id: 1

F+cnj=F leave Peter house • pm: 1 pm:l pm:l (1 then 2) id: 1 id:2

street pm:2 id: 3

In German, this universal type of navigation is realized as follows:

C.4.3 WORD ORDER OF ADVERBIAL EMBEDDING IN GERMAN

Peter Oberquert, nachdem er das Haus verlassen hat, die StraBe. (Peter crosses, after he the house left-has, the street.)

realization F+A=F t buffer: [Verb]+ [NP1] ==> [Verb] [NPl]

realization F+cnj=F 1 t 2t buffer: *[NPl] [Verb]+ [Verb'] ==> *[NP1] [Verb]., [CNJ] [Verb']

realization F+A=F t buffer: *[NP1] *[Verb]., *[CNJ] [Verb']+ [NP1'] ==> *[NP1] *[Verb]., *[CNJ] [Verb'] [NP1']

F+A=F realization buffer: *[NP1] *[Verb]., *[CNJ] *[NP1'] [Verb']+ [NP2'] 2t It

==> *[NPl] *[Verb]. *[CNJ] *[NP1'] [Verb'] [NP2']

F+A=F realization buffer: *[NP1] *[Verb]. *[CNJ] *[NP1'] *[NP2'] *[Verb']+ [NP2] t

==> *[NPl] *[Verb]. *[CNJ] *[NP1'l *[NP2'] *[Verb'] [NP2]

The language-dependent interpretation of F+cnj=F extracts the conjunction CNJ from the functor and produces a language-specific surface in accordance with the distinc­tions presented in C.4.1. Depending on the direction in which proposition 1 and 2 are traversed in the word bank 24.1.11 there are numerous additional types of adverbial embedding navigation. Their English surface reflexes are listed in C.1.3.

C.S Multiple center embeddings

Marking the beginning of elementary propositions by F+A=A and F+cnj=F leaving the return marker T is sufficient even in multiple embeddings.

538 C. Subordinating navigation in the speaker mode

C.5.1 MULTIPLE CENTER EMBEDDINGS IN GERMAN

Peter, der den Salat, den er gegessen hatte, bezahlt hatte, verlieB das Restaurant. (Peter, who the salad, which he eaten-had, paid-had, left the restaurant.)

'Yleave prn:7

Peter prn:7 id: 1

A+id=F: 'Ypay salad.&. prn:6 prn:6

id:5 Teat prn: 5

restaurant .&.prn: 7

Peter.&. id: 4 id:l

Each new embedded proposition allows additional embeddings. However, as soon as an embedded proposition has been traversed completely, it is the tum of the remaining proplets of the next higher proposition to be traversed. When a proposition has been traversed completely is determined by the word bank's traversal counters.

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Name Index

Aho, A.V. & J.D. Ullman, 183, 253 Aho, A.V., P. Weinberger, & B.W. Kern­

ingham, 253 Aho, A.V., J.E. Hopcroft, & J.D. Ull-

man, 270 Ajdukiewicz, K., 132, 133 Anderson, J.R., 54, 67, 99, 399 Anderson, J.R. & G.H. Bower, 99, 401 Appelt, D, 485 Applegate, D., 219, 227 Aristotle, 20, 61, 120, 402, 403, 436 Austin, J.L., 61, 84, 85, 92

Btihler, K., vii, 90, 91, 93, 101 Bach, E., 179 Bach, J.S., 75 Bar-Hillel, Y., 20, 132-134, 141, 158,

168 Barcan-Marcus, R., 88 Barthes, R., 100 Barton, G., R.C. Berwick, & E.S. Ris-

tad, 161, 255 Barwise, J., & J. Perry, 400 Bergenholtz, H., 290 Berkeley, G., 60 Berwick, R.C., & A.S. Weinberg, 170,

195 Beutel, B., 283, 287 Bi Sheng, 26 Bibel, W., 491 Bierwisch, M., 179 Blair, D.C., & Maron, M.E., 36, 37,

290

Bloomfield, L., 130, 155, 303, 319 Bochvar, D.A., 403 Bohr, N., 5 Broker, N., 283 Bresnan, J., 83 Briandais, R., 270 Brooks, R.A., 67 Brown, P., & V. della Pietra et al., 296 Burgess, A., 102 Burnard, L., 298 Burnard, L., & C. M. Sperberg-McQueen,

29 Busemann, 485 Bybee, J.L., 269

Carnap, R., 392-396, 399,400,495 Casanova, G., 117 Chafe, W., 60 Chomsky, N., vii, 82, 129, 141, 146,

158-160, 166, 171, 178, 179, 418

Church, A., 170 Church, K.& L.R. Mercer, 296 Cole, R., viii, 25 Collins, A.M., & E.F. Loftus, 99 Condon, E.U., 295

Date, C.J., 14 Davidson, D., 386 DeRose, S., 296 Dorr, B., 479 Dowty, D., 496 Dretsky, F., 91

560 Name Index

Dreyfus, H., 68

Earley, J., 145, 173, 212, 227 Eco, U., 91 Ellis, C., 260, 283 Elmasri, R., & S.B. Navathe, 14, 438 Estoup, J.B., 295

Fodor, J., 64, 65 Fraassen, B. van, 404 Francis, W.N., 296 Francis, W.N., & H. Kucera, 241, 245 Frankenstein, Dr., 4 Fredk:in, E., 270 Frege, G., vii, 69,76-78, 88, 385, 391-

395,399,400,495 Funge, J., X. Tu, & D. Terzopoulos,

465

Gaifman, C., 168 Garey, M.R., & D.S. Johnson, 145, 227 Garside, R., G. Leech, & G. Sampson,

296 Gazdar, G., 129, 166 Gazdar, G., E. Klein, G. Pullum & I.

Sag, 83 Geach, P., 132, 133 Ginsberg, S., 229 Girard, R., 4 Giv6n, T., 6, 60, 120 Goldfarb, C.F., 28 Greenbaum, S., & Yibin, N., 296 Greenberg, J., 303,310 Greibach, S., 223 Grice, P., vii, 77, 84, 85, 88, 91, 92 Griffith, T., & S. Petrick, 212 Gupta, A., 386 Gutenberg, J., 26

Haiman, J., 60 Halliday, M.A.K., 6, 129 Hanrieder, G., 288 Harman, G., 166

Harris, Z., 141, 155, 158, 168 Harrison, M., 126, 148, 208, 229 Hayashi, T., 229 Herwijnen, E. van, 28 Herzberger, H.G., 386 Hess, K., J. Brustkem & W. Lenders,

290 Hockett, C.F., 168 Hofland, K., & S. Johansson, 289 Hopcroft, J.E., & J.D. Ullman, 142,

143, 147, 149-152, 173, 175, 204,207,208,214,216,225-227,229,238

Hovy, E., 485 Rubel, D.H., & T.N. Wiesel, 60 Humboldt, W.v., 100 Hume, D., 61 Hurford, J., M. Studdert-Kennedy, &

C. Knight, 58 Hutchins, W.J., 43

Illingworth, 28

Jackendoff, R., 113, 447 Jespersen, 0., 286 Johannson, S., 289 Johnson-Laird, P., 60, 400 Joos, M., 295 Joshi, A.K., L.S. Levy & M. Takahashi,

229 Jurafsky, D., & J. Martin, 40

Kaeding, W., 289, 300 Kamp, H., & U. Reyle, 400 Katz, J., & P. Postal, 81 Kaufmann, T., 283 Kay, M., 175 Kepler, J., 7 Kim, D-C., 26 Kim, S., 269 Knuth, D.E., 29,270 Koskenniemi, K., 254 Kripke, S., 105, 386, 396, 411

Kucera, H., & W.N. Francis, 289, 290, 300

Lakoff, G., 113, 302, 402 Lakoff, G., & M. Johnson, 443 Lamb, S., 129 Lambek, J., 132 Langacker, R., 55, 113 Lappin, S., 467 Lesniewski, S., 132, 383, 384 Lee, K.-Y., 283 Leech, G., R. Garside & E. Atwell, 296 Leech, G.N., 289, 298 Lees, R.B., 115 Leidner, J., 268 Levinson, S.C., 84, 102 Lieb, H.-H., 6 Locke, J., 60, 61 Lorenz, 0., 268 Loyall, A. B., & J. Bates, 465 Lucas, G., 4 Ludlow, P., 446 Lukasiewicz, J., 403, 404 Lyons, J., 116

MacColl, H., 403 MacWhinney, B., 93, 113, 256, 452 Maes, P., 15 Mahlow, C., 268 Mandelbrot, B., 295 Marr, D., 54, 67 Marshall, I., 296 Marshall, I., 296 Marx Brothers, 120 Matthews, P.H., 243 McCawley, J.D., 179 McDonald, D., 485 McKeown, K., 485 Meier, 289 Mel'cuk, I. A., & A. Polguere, 479 Miller, G., 327 Miller, G., & N. Chomsky, 170

Name Index 561

Montague, R., 73, 81, 133, 136, 137, 386,390,391,395,396,399, 400,411,416,417,419,467, 468

Nakayama, K., H. Zijiang, & Shinsuke Shimojo, 54

Nasr, A., 0. Rambow, M. Palmer, & J. Rosenzweig, 4 79

Neisser, U., 60 Newell, A., & H. Simon, 15, 52, 67,

399,400 Newton, I., 7

Ockham, W. of, 83 Ogden, C.K.& LA. Richards, 60 Oostdijk, N., 291 Oostdijk, N., & P. de Haan, 290

Palmer, S., 60 Park, Y-W., 26 Pascal, B., 380 Paul, H., 302 Peirce, C.S., vii, 56, 60, 403 Pereira, F., & Warren, D., 429 Peters, S., & R. Ritchie, 129, 160 Plato, 90 Pollard, C., & I. Sag, 83, 115 Post, E., 141, 170, 182 Ptolemy, 5 Putnam, H., 400, 411

Quine, W.v.O., 390-392,394,399,414

Rabin, M.O., & D. Scott, 214 Reddy, D.R., et al., 15, 400 Reichenbach, H., 448, 449 Reiss, P., 462 Reiter, E., A. Cawsey, L. Osman, & Y.

Roff, 485 Reithinger, N., 485 Rescher, N., 403 Rickel, J., & W.L. Johnson, 465

562 Name Index

Ritchie, R., 160 Rogier van der Weyden, 118 Roukos, S., 339 Rumelhart, D.E., 59, 67 Rumelhart, D.E., P. Smolensky, J. Mc­

Clelland, & G.E. Hinton, 99 Russell, B., 391, 392, 399, 400 Russell, S.J., & P. Norvig, 493

Salton, G., 35, 49 Sapir, E., 100, 242 Saussure, F. de, vii, 97, 98, 103, 108,

122 Schuller, G., 283 Schank,R.C.,399,400 Schank, R.C., & R. Abelson, 400 Schulze, M., 145 Scott, D., 205 Scott, D., & C. Strachey, 375 Searle, J.R., 17, 84, 92 Searle, J.R., & D. Vanderweken, 86 Sells, P., 81 Shannon, C.E., & W. Weaver, vii, 91 Sharman, R., 296 Shieber, S., 166 Shieber, S., S. Stucky, H. Uszkoreit, &

J. Robinson, 166 Sinclair, J., 242 Skinner, B.F., 461 Sowa, J.P., 442, 457 Sperling, G., 60 St.Laurent, S., 28 Sternberger, P.J., & B. MacWhinney,

249 Stubert, B., 215, 217, 218, 228

Tarr, M.J., & H.H. Btilthoff, 54 Tarski, A., viii, 377-384, 386, 396, 409,

411, 413, 415, 416 Tesniere, L., 129, 304 Thiel, C., 383 Trakhtenbrod, 424 Turing, A.M., 1

Uszkoreit, H., & S. Peters, 129, 166

Valiant, L.G., 213 Vasil'ev, A., 403

Wagner, R., 117 Wahlster, W., 24 Wahrig, G., 75, 302 Wall, L., & R.L. Schwartz, 253 Weizenbaum, J., 2, 40 Wetzel, C., 268, 270 Wexelblat, A., 16 Weyhrauch, R., 375 Wheeler,P., & V. Lawson, 43 Whorf, B.L., 100 Winograd, T., 68, 73, 399, 400 Winston, P.H. & B.K. Hom, 434 Wittgenstein, L., 77, 84, 117,400 Wloka, D.W., 15 Wright, 0., & W., 4

Yang, D-W., 82

Zeevat H., E. Klein & J. Calder, 288 Zierl, M., 291 Zipf, G.K., 294, 295 Zue, V., R. Cole, & W. Ward, 25

Subject Index

A-( absolute-) inference, 501, 502, 504 A-LAGs (unrestricted LA-grammars),

151,205,208 A-proplet, 442, 444, 468, 478, 479 abacus, 372 abbreviation, 413-415 ablaut, 249, 261 aborigines, 242 absolute inference, 444, 446, 459, 494 absolute proposition, 442, 443, 446, 493,

494 absolute type transparency, 170, 191 abstract automaton, 144, 169, 212 abstract ideas, 60 Ackermann function, 152 action, 58, 72, 436

component, 65, 72 gripping, 58 locomotion, 65 parameter, 448

additive copying, 481 ad hoc solutions, 139 adjective, 62, 138, 246, 437

adnominal/adverbial, 62 agreement, 325, 345, 354 allomorphs, 266 inflection, 275 proplet, 522

agglutinative language, 303 agreeing lists, 358 agreement, 139, 195, 197, 285, 303,

definition-based, 317, 332

307,326,501 external/internal, 321, 322 identity-based, 310, 322 violation, 307

algebraic definition, 128 C-grammar, 133 LA-grammar, 187 PS-grammar, 141

allo-rule, 259, 261 allograph, 248 allomorph, 24 7, 249 allomorph method, 255, 256, 259 allomorph quotient, 266, 268 allomorph reduction, 254 allophone, 248 alphabet, 126 alphabetical word form list, 30, 297 alternative LA- and PS-classifications,

224 ambiguity, 45

lexical, 136, 139, 210, 285 of natural language, 232 pragmatic, 232 pseudo-, 235 (non)global, 210 (non)recursive, 211, 216, 218 SR-recursive, 218 semantic, 232 syntactic, 45, 79, 210, 216, 232

amount parameter, 206, 220 analogous reconstruction, 431, 448 analytic language, 303,

564 Subject Index

analytic philosophy, 63, 73, 76, 105, 392,400

analyzed surface, 79, 372 anaphoric interpretation, 111 antecedent, 111 application, 282 arbitrariness of signs, 108 argument, 61, 436, 437 artificial intelligence, 13 assignment (of a function), 133, 137,

378 assignment (of a meaning), 372, 378 astronomy, 7 attribute-value structures, 287 automata theory, 204 automated cinematography, 465 automatic word form recognition, 248,

251-257, 299, 308, autonomous agent, 15, 66 autonomous control structure, 461 autonomy from the metalanguage, 64,

382 auxiliary

English, 329 German, 354, 355

average sentence length, 145 AWK (Aho-Weinberger-Kerningham),

253 axiomatization, 6, 7

B-LAGs (bounded LAGs), 151, 206, 208

Bach-Peters sentence, 159 backward navigation, 457, 458 balance principle, 462, 463 balanced corpus, 145 bandwidth, 91 Barriers (Chomsky), 128 base form entry, 260 base form lexicon, 268 behavior control component, 66, 461-

464,466

behavior test, 54 behaviorism, 1, 6, 86, 99 best match, 92 bidirectional C-grarnmar, 134, 168 bivalent logic, 402 blocks world, 400 BNC (British Nat. Corpus), 291, 292 Boolean satisfiability, 226 bottom-up amalgamating, 185 bottom-up derivation, 172 bottom-up left-associative, 185 bound morpheme, 250, 256 breadth-first format, 192 Brown corpus, 289, 339 bus bell model, 91

C-grammar (categorial-grammar), 6, 128, 132,158,195,305

algebraic definition, 133 akbk, 135 rule schema, 185 natural language, 136-139

C-LAGs (constant LA-grammars), 207, 208

C1-LAG, 147, 215, 216 C2-LAG, 149, 215, 216-218 C3-LAG, 215, 218 C3PO, 4 C-proplet (context-proplet), 442, 460,

487,504 calculus of classes, 379 calendar, 44 7 canceling valency positions, 480 cardinality, 485 Cartesian framework, 446 cataphoric interpretation, 112 categorial operation, 185, 187, 189, 197 categorization, 251

distinctive, 347 exhaustive, 346

category, 108 category segment, 187, 196, 307

causal chain, 105, 411 CCG (Combinatory Categorial Gram­

mar), 6, 129 CF-LAG (context-free LA-grammar),

208 cf-structure in German, 165 characteristic function, 137 chart parser, 175 chess, 15, 400 Chinese, 303 Chomsky hierarchy, 146 Chomsky-Schtitzenberger Theorem, 148 class of natural language, 146, 167 classes of languages, 143 classic AI, 15, 66 classic database, 438 classical inferences, 491 CLAWS! (BNC-tagger), 296 CLAWS4 (BNC-tagger), 297 closed word classes, 62, 244 cognition test, 54 cognitive modeling, 465, 466 cognitive science, 13 coherence, 63,465,466,485,486 cohesion, 46, 100, 310, 485 CoL (Hausser 1989a), 4, 53, 77, 99,

100, 102, 107, 137, 147, 149, 157,189,193,198,204,207-209,215-217,234,322,329, 366,376,382,408,420,422, 432,435

collocation, 46 color reader, 53 combi-rules, 272, 273 combination, 375 combination principles

morphology, 242 syntax, 303

command, 373 communication prototype, 90, 94 compatible input conditions, 209, 216 competence, 18, 178

Subject Index 565

complete analysis, 188 completeness (tracking principle), 464,

534 completion, 440-442 completor operation (Earley), 173, 175 complex verb forms

English, 330 German, 355

complexity, 85, 169 complexity degrees, 224 complexity of natural language, 236 components of grammar, 18 composition (of semantics), 242 compositional semantics, 503 computational complexity, 143 computational linguistics, 18 computational! y intractable, 151 computer science, 13, 163, 176 concatenation, 250, 255, 275 concatenation operator, 184 concept-based indexing, 39 conceptual graph theory, 442, 455 conceptual derivation order, 185 conceptualization, 9, 99, 441, 485 concreteness, 419 conjectures, 129 conjunction, 463, 489 CoNSem hypothesis, 425 constant categorial operations, 207 constituent structure, 9, 153, 155, 179,

195,242,422,456 constituent structure paradox, 157, 158,

456 constraints, 139 CoNSyx hypothesis, 236 content analysis, 22 content unit, 74, 97, 105, 437, 439-

443,456,479,496,503 context, 55, 432, 434, 436 contextual cognition, 66, 465 context-free languages, 143, 208 context-free PS-grammar, 142

566 Subject Index

context-free structure, 149 context-sensitive languages, 143, 207 context-sensitive PS-grammar, 142 context -sensitive structure, 149 continuation predicates, 437 continuous speech, 25 control symbols, 28 convention, 84, 85, 116 convention-based assignment, 373 conventionalists, 60, 115 convergence, 179 conversational implicature, 85 copying operations of semantic inter-

pretation, 480 core corpus, 296 coreference, Ill corpus, 288 cross-sentential coreference, 113 cs-structure in Swiss German, 166 CUG (Categorial Unification Grammar),

129 CURIOUS, 52, 55, 71, 399, 401, 414,

416 cyclical external events, 44 7 CYK algorithm, 165, 175

database, 14, 17, 72 textual, 33 classic, 428 knowledge base, 434

database component, 65 database metaphor, 430, 431 database semantics, 429, 455, 466, 486 DB (database) interaction and NL (nat.

lang.) communication, 431 DBL-LEX, 468, 478 DCG (Definite Clause Grammar), 429 debugging, 176,200,273 decidability, 204 declarative main clause, 352 declarative specification, 164, 130 deep structure, 81, 159, 160

defaults, 435 default rule, 260 degrees of inflectional regularity, 263 delimitation of components, 301 demons, 435 demotivation, 115, 117 denotation, 376, 377, 433 dependency grammar, 129 depth-first format, 192 derivation, 242 derived formalism, 128, 160 descriptive adequacy, 131 descriptive aporia, 179, 404, 429, 433,

436 desiderata of generative grammar, 132,

180 desktop publishing, 29 DET+N,483 determiner, 138, 285, 481

English, 322 German, 344

deterministic automata, 213 deterministic context-free languages, 147 detransitivization, 304 DI-inference (view-Dependent to view­

Independent), 496, 498, 499, 502,504

diagonalization, 150 dialog, 458 dialog system, 22 direct observation, 446, 450 direct machine translation, 42 discontinuous elements, 157, 179, 199,

456 discourse semantics, 400 displaced reference, 17 distinctive categorization, 244, 246, 285 distribution test, 155, 242 domain, 133 domain independence, 25 dominance, 153 door bell model, 91

double aspect theory, 63, 396 double ended queue, 228 DSPACE (deterministic space), 213 DTD (document type definition), 29 DTIME (deterministic time), 213, 214

E-inference (episodic inference), 444, 446,459,468,493,494,501, 502,504

E-infbe• 444, 494 E-proplet, 442, 444, 461,468, 478, 479 Earley algorithm, 165, 173, 175, 212 effective computability, 170 elementary base form, 245 elementary formalisms, 128, 141 elementary lexicon, 259 elementary proposition, 435, 466 Eliza program, 2, 40 embarrassment of riches, 156, 179,404,

429, 433-436, embedded subclause, 530 embedding, 432 embedding navigation

adnominal, 535 adverbial, 537

empiricists, 60 energy supply, 462 entity, 137 entry context, 94, 95 Epimenides paradox, 383, 384, 386,

413,414 episodic inference, see E-inference episodic proposition, 443, 442, 446, 493 epr-navigation, 481 epsilon-free DPDA (deterministic push-

down automaton), 147 equivalence, 80 Eskimo, 303 EST (extended standard theory), 6, 128 evaluation criteria, 53 evolution, 115 exclusive copying, 481

Subject Index 567

exhaustive categorization, 244, 246, 285 existential generalization, 390 explicit hypothesis, 17, 130, 467 exponential complexity, 144, 145 extensional context, 392, 495 external action, 459 external connections, 448 external recognition, 459 extraction, 432 extrapropositional navigation, 457, 458,

460 extrapropositional relation, 62,437, 441,

443,485,503

face to face communication, 52, 94 FAHQT,43 feature structure, 436, 437, 442-445 fictitious STAR-point, 96 field of referents

English, 326 German, 347

final state, 187 finite state automata, 214 finite state back bone

akbkck, 189

LA-D4, 365 LA-El & LA-E1.5, 335 LA-E2, 333 LA-E3, 338

finite state transition network, 189 Finnegan's Wake, 5 first-order predicate calculus, 398 flight of birds, 4 focus point, 99, 459, 461 formal translation, 390 formation of classes, 59 fragment, 136,386,467,468,477-479,

483, 487, 489, frame, 433 free association, 464 free monoid, 126, 127, 143 free morpheme, 250

568 Subject Index

Frege's principle, 77, 78, 80, 82, 416, 417,420

frequency (tracking principle), 480 frequency analysis, 289 frequency distribution, 292 frequency list, 296 FUG (Functional Unification Grammar),

129 full-form method, 254 function, 133 function word, 62, 245 functor, 61 functor-argument structure, 20, 133, 138,

304,422,441,456,503 future-contingent, 402, 403 FV+MAIN, 482

garden path sentence, 211 GB (Government and Binding), 6, 81,

128 GdCL (Hausser 2000), 529 general purpose machine, 15 generation, 127 generative capacity, 143,203,204,208 generative grammar, 168, 127 Generative Semantics, 128 genre, 289 geometry, 397 global ambiguity, 210 glue, 108, 116 GPSG (Generalized Phrase Structure

Grammar), 6, 81, 83, 128, 160, 161, 166

grammar system, 281, 316 grammatically well-formed, 125, 130 Grice's meaning definition, 84 Gutenberg Revolution, 26

hack, 130,283 halting problem, 204 hammer, 119 Hangul, 26 hapax legomena, 292

hard copy, 27 hash table, 257 HCFL (hardest context-free language),

218 hearer mode, 70-72,97,440,445,470,

472,481-485,502 HEARSAY, 400 Hidden Markov Model, 296 hierarchy

LA-grammar, 224 PS-grammar, 224

hierarchy of metalanguages, 381 hieroglyph, 120 homomorphism, 60, 417 homunculus, 4, 61 how to say it, 41, 99, 485, 486 HPSG (Head-driven Phrase Structure

Grammar), 6, 81, 83, 128, 160, 161,437

human-computer communication, 13, 14, 16, 17

Humboldt -Sapir-Whorf hypothesis, 100 hypotactic navigation, 457, 458, 534,

537

1-conceptzoc (instantiated token), 56, 58, 70,442

I-formz0 c, 70, 71 !-proposition, 61, 62, 72 IBM-VoiceType, 25 icon, 59, 114 iconic reference, 105, 106, 116, 119,

497 iconic use, 118 id-navigation (identity-based navigation),

481 ID-inference (view-Independent to view­

Dependent), 498,496,499,501, 504

identical input conditions, 210 identity, 437, 456, 485 identity mapping, 419

ideographs, 120 idiom, 46, 302 image, 24, 54 imitation game, 1 immediate constituent analysis, 155 immediate context, 466, 496 immediate obviousness, 380 immediate perception, 465 immediate reference, 75, 90 immediate reference, 75, 90, 430, 449,

471,472 implementation, 282 implication, 495 inchoative verbs, 496 incoherence, 466 incompatible input conditions, 190, 209 incompleteness, 131 incorrectness, 131 incremental delay, 485 index languages, 229 indexical, 105, 107, 326, 496 indexical reference, 105, 106, 110-114,

497 indexing and retrieval, 33, 34,431,441 inference, 9, 115, 444, 447, 463

A-infbe· 494 conjunction, 492 DI-inh, 498 Dl-infR, 499 E-infbe• 493, 494 ID-inh, 498 ID-infR, 499 modus ponens, 492

inflection, 242, 246, 457 inflectional language, 303 inflectional paradigm, 242 informal translation, 390 information theory, 91 inherent complexity, 170 initial reference, 108 initial state, 187 innate human language faculty, 83, 176

Subject Index 569

input-output equivalence, 171, 176, 186 integrated architecture, 485 intellectual stimulation, 462 intension, 392, 394 intensional context, 391, 395,400,495 intensional logic, 395, 468 intention, 84, 86, 118 interactive architecture, 485 interlingua-based MT, 47, 48 intermediate expressions, 197, 324 internal matching, 73, 374, 430, 442 internal recognition, 459 interrogative sentence

English, 336 German, 360

interrogator, 1 intrapropositional relation, 437, 443 intrapropositional navigation, 457, 458,

460 irregular inflectional paradigm, 263 isolating language, 303

key, 253 Kleene closure, 126 knowledge base, 438

L-proplet, 442,444,445, 468, 478, 481, 487,497,502

LA-generator, 193 LA-grammar, 9, 128, 183, 305, 459

algebraic definition, 187 ai!, 215 akbk, 188 akbkck, 188 akbkckdkek 215 , akbmck·m, 215 a2;, 216 A-LAG (unrestricted LAG), 205 B-LAG (bounded LAG), 206 C-LAG (constant LAG), 208 derivation, 190 hierarchy, 208

570 Subject Index

L~st• 217 L~ast• 215 Ln0 , 228 Lsquare• 215 LA-D1, 313 LA-D2, 350 LA-D3, 358 LA-D4, 364 LA-E1, 316 LA-E2, 331 LA-E3, 338 LA-E4, 522 LA-INPUT, 479, 480, 489 LA-MOTOR, 460, 461,464,468,

470,486,487 LA-OUTPUT, 445, 487-489, 501 LA-plaster, 308 LA-Q1, 491 LA-Q2, 492 natural language, 195 restriction types, 220 rule schema, 185 SubsetSum, 219 unrestricted, 203-205 wk~3.217

WW,218 wwR,217

LA-Morph, 273 LA-search, 489 LA-syntax

clause, 356 subclause, 359

LAMA, 283 language, 125, 168 language classes, 169, 224 language hierarchy

LA-grammar, 147, 149, 151, 152, 215,220

PS-grammar, 146, 151, 204, 221 language interpretation, 70-72, 97,440,

445,470,472,481-485,502 language pair, 41

language processing, 70 language production, 70-72, 97, 439,

445,471,472,485-489,501 language typology, 303, 310, 343 language-based cognition, 454 LAP (LA-parser), 284, 286 Jt.Tpc, 29 LAUG (LA-unification grammar), 288 law of excluded middle, 402 LBA (linear bounded automata), 207 LEA-problem, 214, 215 Lcf (context-free languages), 231 Ldcf (deterministic cont.-free lang.), 231 leading ideas, 82 left-associative derivation order, 183,

251 lemma, 252, 438 lemmatization, 252 letter, 23, 119 letter tree, 270 letter-based corpus analysis, 295 letter-based indexing, 38 letter-based method, 31 level of abstraction, 4, 7, 8 lexeme, 243 lexical ambiguity, 136, 139, 210, 285 lexical description, 252 lexical frames, 478 lexical gap, 46 lexical look-up, 250 lexical semantics, 503 lexicalist approach, 136 lexicalization, 457 lexicography, 19 lexicology, 19 lexicon, 19, 37, 126, 187 LFG (Lexical Functional Grammar), 6,

81, 83, 128, 160, 161 Limas corpus, 145, 290 linear complexity, 144 linear search, 253 linear time, 190, 215

linguistic generalizations, 166 linguistic pragmatics, 89 LISP,284,375,434 list-based matching, 287 literal meaning, 21, 75, 503 LOB corpus, 289 logic, 127 logical language, 376 logical meaning, 389 logical proposition, 375, 409 logical semantics, 94, 371, 372, 375-

399,402 logical syntax, 377 logical truth, 410

M-concept (matching type), 57, 70,442 M-form, 70, 71 M-proposition, 72 machine aided translation, 47 machine translation, 22 Malaga,284,287,314 many-valued logic, 402 mapping relations between N(atural),

L(ogical), andP(rogramming) languages, 374

matching relation, 441, 445 mathematical constructivism, 392 mathematical properties, 130 mathematical realism, 392, 399, 424 meaning1 , 76, 77,85,108,500-503 meaning2, 76, 77,85,500-503 media of language, 14, 18, 23-31, 70,

71, 100, 108, 115, 119, 248, 446,448,465,466

mediated reference, 75, 90, 93, 95, 110, 449,470,471

mediated subcontext, 466 member record, 438 mental models, 400 metalanguage, 377, 412, 466 metalanguage-based semantics, 398 metaphorical reference, 108, 458

Subject Index 571

metarules, 129, 166 methodology, 3, 7, 21, 128, 130 minimal complexity, 170 mistranslation, 43 modal logic, 394, 396 model, 376, 380, 393, 412 model-theoretic semantics, 1, 6, 86, 137,

375-377,380,395,397,398, 432,433,466,512

modifier, 61, 436, 437 modularity, 283 Montague grammar, 73, 81, 86, 129,

282,390,395,416,433,456, 467,477

morpheme, 247,249 morpheme method, 254, 256 morphologically-based indexing, 38 morphology, 19, 37, 247 morphology parsers, 163 motivated, 115 motivational chain, 120 motor algorithm, 459 movement test, 155 multicat, 236, 286, 323 multiple morphological analysis, 267 multiple segmentations, 270

name-based reference, 105-107, 114-117' 496, 497

nativism, 1, 6, 81, 83, 86, 114, 156, 160,161,176,178,179,312, 418,512

natural classes, 411 natural semantics, 372, 373 natural truth, 410 naturalists, 58, 115 navigation, 457, 458 necessity, 394, 396 need parameter, 462, 469 need vector, 462, 463 negative testing, 200 neologism, 247, 254

572 Subject Index

network database, 438, 439 neutral element, 126 new sentence start, 184 NEWCAT (Hausser 1986), 191, 192,

195,198,201,366 next word, 184 NLG (nat. lang. generation), 109, 485 noise, 91 NOM+FV,482 nominal proplet, 522 nominal valency fillers, 326 nominalism, 399 non-bivalent logic, 402 non-elementary base forms, 267 non-existing objects, 391 non-identity, 438 nonclassic database, 438 nonconstant categorial operations, 207 nondeterministic automata, 213 nonglobal ambiguity, 210 nonlinguistic pragmatics, 89 nonrecursive ambiguity, 211, 216 nontangling condition, 154 nonterminal rule, 153 nonterminal symbol, 142 nonverbal communication, 106 normalized allomorph quotient, 269 notational variants, 168 noun,62, 137

agreement, 322 allomorphs, 264 category segments, 277 inflection, 275, 276 valency filler, 317

noun-noun compounds, 246 nouvelle AI, 15, 66 NP (nondeterministic polynomial time),

225 NP-complete, 219, 226, 255 NSPACE,213 NTIME, 213, 214 number of rule applications, 209

number parameter, 206, 220

object, 61, 437 object language, 377,386,415 Occam's razor, 83 OCR (optical character recognition),

24 off-the-shelf, 41 on-line

text, 29 lexicon, 251

onomatopoeia, 115 ontogenetic development, 67, 71, 90,

106 ontology, 62, 106, 467

basic problem of model theory, 397 [±constructive], 397, 401 [±sense], 394 [±sense,±constructive], 399-400,

404,411,412,424,433,436 opaque context, 391-395, 400 open word classes, 62, 244 operator, 62 opportunistic corpora, 291 ordinary language philosophy, 6, 84,

86, organon model, 90 origo,93 orthogonal £dcf, £cf, C 1, C2 and C3

classification, 231 orthogonal C and cf classification, 230 overgeneration, 131 owner record, 438

P (deterministic polynomial time), 225 paleolithic wedge, 119 paraphrase, 79, 80 paratactic navigation, 457, 458, 533 parser, 3, 7, 21, 163, 169, 171 parser trace, 176 part of speech, 21, 107, 244, 478 partial function, 203 partial recursive function, 208

pattern matching, 34, 134, 207, 257, 312-315,323-325,331,337, 345,356-358,361-364

perception, 56 performance, 9, 18 PERL, 253 permanent sign, 448, 449, 451 perplexity, 339 phlogiston, 412 phonetics, 18 phonology, 18 phrase structure grammar, 141 phylogenetic development, 67, 71, 90,

106 physical symbol system, 66 pictogram, 120 picture, 118 pipeline architecture, 485 planning hierarchy, 486 plural morpheme, 250 'point of reference', 449 'point of speech', 447 'point of the event', 44 7 pointer, 105 pointing area, 110 polynomial complexity, 144, 145 polynomial time, 217 polysynthetic language, 303 positioning, 115, 118 positive closure, 126 possibility, 394 possible continuations, 181, 184, 186,

197,420,439 possible substitutions, 181, 184, 186,

197 possible worlds, 394 Post production system, 141, 171 postcedent, 112 postconceptual processing, 468 postquery processing, 39 powering the navigation, 459 pragmatic ambiguity, 232

Subject Index 573

pragmatic anchoring, 96 pragmatic wastebasket, 21 pragmatics, 20, 37, 89-92, 432, 503

PoP-1, 77 PoP-2, 94 PoP-3, 98 PoP-4, 105 PoP-5, 105 PoP-6, 105 PoP-7, 107

precedence, 153 precision, 35, 37, 39 preconceptual processing, 468 predicate calculus, 383, 386, 398, 429,

456,493 predictor operation (Earley), 173, 174 preposition, 307, 403 prepositional phrases, 234 preverbal position, 352 primary positioning, 94 primitive operation, 144, 169, 212 primitive recursive function, 208 principles of SLIM, 8 printing with movable letters, 26 problem space, 52, 400 procedural implementation, 164 procedural semantics, 373, 398, 430 productive, 242 programming language, 14,373 programming semantics, 371, 372 Prolog, 375, 383, 429 pronominal epithet, 113 pronouns, 110 property, 62 proplet, 437, 442 proposition, 9, 61, 72, 376, 390, 393,

394,396,404,408,463 graphical presentation, 62, 436, 456,

516 number, 437, 523

propositional attitudes, 395, 396, 398, 411

574 Subject Index

calculus, 210, 398, 491 content, 456 elements, 62, 107

prototype of communication, 52 PS-grammar, 128, 142

algebraic definition 142 {a, b}+, 147 akb3k, 148 akbk, 127 akbkck, 150 ambk (k,m 2: 1), 147 abk (k 2: 1), 146 Lno, 227 natural language, 152 rule schema, 185 wwR, 148

pseudo-ambiguity, 235 psychologism, 76, 400 PTQ (Montague 1974, Ch. 8), 395, 396,

411, 467, 468 purpose, 462 pumping lemma, 14 7, 149 pure indexicals, 110 pushdown automaton, 208, 214 puzzles, 392, 395

quantifier scope, 456 quantifier, 57, 485 Quechua, 131 query, 35 query expansion, 38 question, 491

rac-(recognition-action-recognition-) se-quence,461-463,469

railroad system, 456 range, 133 rank, 292 realistic vocabulary, 25 realization, 70, 99, 488 realizing a navigation, 457 recall, 35, 36

receiver, 90, 91 recency (tracking principle), 464 recognition, 24, 56, 58, 70, 430

colors, 55 component, 65, 72 parameters, 58, 70, 430 square, 53, 54

recognition algorithm, 251, 252, 254 reconstructed pattern, 54 reconstruction (between semantics), 375,

390 record, 14, 438 recoverability of deletions, 129, 159 recursion, 127 recursion theory, 171 recursive ambiguity, 211, 216, 218 recursive function, 203 recursive languages, 151, 190,203,204 recursive valuation schemata, 386 recursively enumerable languages, 143,

152,205 reference, 72, 391, 497 reference corpus, 40, 292 reference mechanisms, 105, 106, 497 reflex, 496 regular expressions, 34 regular inflectional paradigm, 263 regular languages, 143, 208 regular PS-grammar, 143 relation, 61, 437 relational database, 438 relative clause, 456, 534 relative entropy, 91 repeatability of experiments, 6 repeated reference, 108, Ill repeated training, 461 replication (between semantics), 374 representative corpus, 145 REST (Revised Extended Standard The­

ory), 6, 128 restaurant script, 400 restricted language, 47

result segment, 304 retrieval, 35 reverse engineering, 416 rewrite system, 141, 142, 170 rhetoric, 20 rhetorical goal, 458 right-associative, 183 rigid designators, 105 robot,9, 15, 17,52-54,58,64,67, 110,

410,414,415,437,470, robustness, 25 rock, 119 rotation principle, 120 rough translation, 4 7 rule package, 185, 187 rule restrictions, 142, 220, 225 rule schema

C-grammar, 133, 185 LA-grammar, 185, 187 PS-grammar, 142, 185

Russell paradox, 132

SAM (Shank & Abelson 1977), 400 SAT (Boolean satisfiability), 218 Saussure

first law, 108 second law, 97

SC-I (surface compositionality I) prin­ciple, 80

SC-II (surface compositionality II) prin­ciple, 420

scan operation (Earley), 173, 175 SCG (Hausser 1984a), 80-82, 139, 195,

397,407,419,433,434 scope ambiguities, 456 search proplet, 489 search space, 15, 151 secondary task analysis, 497-499 segmentation, 248, 250, 254 self-reference, 383 semantic ambiguity, 232 semantic doubling, 234

Subject Index 575

semantic interpretation, 137, 441, 480 semantic operations, 457 semantic parsers, 163 semantic significance, 295 semantically interpreted LA-rule, 480 semantics, 20, 37, 137, 371-387, 429-

504 semi-formal grammar systems, 129 semi-irregular inflectional paradigm, 263 semi-regular inflectional paradigm, 263 sense, 391, 394 sentence frame, 153 sentence start, 184 separable prefixes, 261 SGML (standard generalized markup

language), 28, 29, 291 SHRDLU (Winograd 1972), 73, 400 sign-types, 107, 245 signal transmission, 91 single return principle, 217 situation semantics, 400 Slim machine, 9, 99, 399, 452, 455,

467,468 SLIM theory of language, 8, 49, 69,

52,73,84-86, 89,96,98,99, 105, 106, 111, 116, 157, 186, 232,237,244,255,256,366, 374,395,401,409-411,416, 418,424,430,436,452,455, 459,491,504

SLIM 1: contextual recognition, 469 SLIM 2: contextual action, 469 SLIM 3: contextual inference, 470 SLIM 4: interpretation oflanguage, 470 SLIM 5: production of language, 471 SLIM 6: language-controlled action, 471 SLIM 7: commented recognition, 472 SLIM 8: language-controlled recogni-

tion, 472 SLIM 9: commented action, 472 SLIM 10: cognitive stillstand, 473 smart solution, 40, 283, 298, 512

576 Subject Index

soft copy, 27 solid solution, 40, 299 solipsism, 54, 86 Sorites paradox, 402 source language, 42 spatio-temporal indexing, 446, 447 speaker independence, 25 speaker mode, 70-72, 97, 439, 445,

471,472,485-489,501 speech act theory, 6, 84-86, 92, 94,

512 speech recognition, 24-26, 451 speech synthesis, 23, 70, 71, 469 spontaneous reference, 116 spreading activation, 99 SQL (structured query language), 438 SR-recursive ambiguity, 218 ST (standard theory), 6, 128, 159 ST (space-time), 94, 95 STinten 94, 95, 449, 451 STprop• 447, 449, 451 stacking, 138 STAIRS, 36 standard computer, 13, 15-17 Star Wars, 4 STAR (space-time-agent-recipient), 93-

95, 109, 110, 410, 411, 447, 449,451,498,499,501,502

start state, 189 start symbol, 142 state, 185, 189 state set (Earley), 174, 175 statistics, 40, 512 statistical tagging, 296 stemming, 278 'stimulus-response-consequence', 461 strong equivalence, 167 stratificational grammar, 129 structural divergence, 479 structuralism, 6, 86, 156, 242, 250 structured list, 190 subcontext, 63, 432-434, 516

subordinate clause, 353 subset relations between LA- and PS­

grammar, 225 SubsetSum, 218 substitution salva veritate, 390, 394,

396 substitution test, 155 substitution-based derivation, 171, 442 substitutivity of identicals, 390 subtheoretical variants, 284 subtypes of grammar, 169 successful communication, 86,429,431,

441,458 SUNDIAL (Speech Understanding and

DIAlog), 288 supervaluations (van Fraassen), 404 suppletion, 249 surface, 108, 442 surface compositionality, 80, 111, 256,

327,418,501,502 surface structure, 81, 159 syllable, 248 symbol, 73, 105, 107, 116 syntactic ambiguity, 45, 79, 210, 216,

232 syntactic paraphrase, 79 Syntactic Structures, 128 syntactic sugar, 82, 418 syntactic-semantic derivation, 421,480-

485,521-532 syntactically active, 480 syntactically-based indexing, 38 syntagma, 186 syntax, 19,37,301,303-368,449 syntax parsers, 163-202 synthetic language, 303 systemic grammar, 6, 129

T-condition, 378, 380 T-proplet, 497, 501, 502, 504 table, 175 tagset, 296

TAL (Tree Adjoining Language), 229 target language, 42 task analysis, 496-500, 502 task environment, 52, 53, 400 task level decomposition, 16 TEl (Text Encoding Initiative), 29, 291 telegram style, 104 telephone, 449 temperature, 462 temporal landmark, 44 7, 450 temporal logic, 394 terminal symbol, 142, 153, 171 Tpc, 29 text checking, 22 text classification, 39 text processing, 29 text production, 22 text structure, 29 textual database, 21 Theorem 1, 204 Theorem 2, 205 Theorem 3, 211 theoretical linguistics, 17 theory of grammar, 6, 131, 419 theory of language, 6, 7, 23,31 theory of signs, 103 theory of truth, 379, 381, 386, 389,

392,396,397,400 thesaurus, 37 thought, 99,459,464,486,501,502 time complexity, 145 time-linear

analysis, 177 derivation order, 9, 195 homomorphism, 420 structure, 97, 184

token, 56, 70, 84,85,430,437 token line, 437, 438, 450, 519 toothache, 63, 396, 425 top-down derivation, 171, 176 top-down expanding, 185 total recursive function, 203

toy systems, 254 toy worlds, 400 trace

Subject Index 577

Chomsky's, 418 generator, 193 parser, 191

tracking principles, 464 traditional dictionary, 252 traditional grammar, 17 traditional morphology, 242 Trakhtenbrod Theorem, 423 transfer (between semantics), 375 transfer-based MT, 43 transformational grammar, 6, 81, 86,

255,282 transformations, 81, 159, 179 transition (finite state backbone of LAG),

189, 333 transitivization, 304 transmitter, 90, 91 traversal counter, 464, 538 tree

C-grarnmar, 135, 138, 195 LA-grammar, 190, 196 PS-grammar, 152, 154, 157, 158

tree bank, 99 trial and error, 136 trie structure, 270, 271 true sentence, 377 truncation, 37, 278 truth conditions, 84, 375-380, 434, 468 truth predicate, 385, 386 truth value, 137 Turing machine, 169, 204 Turing test, 1, 2 tum taking, 431 Turkish, 303 tutoring, 22 two-valued logic, 402 two-level navigation, 459, 462, 463,

468,470,471,487 [2+1] level structure, 73,106

578 Subject Index

type, 56, 70, 84, 85 type transparency, 170, 175, 178, 186,

213 type-token correlation, 292, 294

UCG (Unification-based Categorial Gram-mar), 6, 129, 288

unambiguous LA-grammars, 190 unanalyzed surface, 79, 417 undecidable, 144, 152 undergeneration, 131 underspecification, 132 uneven context (Frege), 391-395, 400 ungrammatical input, 199, 274 unification, 318 uniqueness (tracking principle), 464 unmotivated (de Saussure), 108 unrestricted LA-grammar, 203-205 unrestricted PS-grammar, 142, 152 unseparable prefixes, 261 upscaling, 175, 200, 344

vacuous T-condition, 381 vagueness,402,404 valency, 195, 197, 303, 440, 480 valency filler

English postverbal, 324 English preverbal, 322 German, postverbal, 346 German, preverbal, 345

valency positions English auxiliary, 329 English main verb, 328 German auxiliary, 354 German main verb, 349

variable, 142 variants of language science, 17 variants of communication, 51 vending machine, 4 verb, 62, 246, 437

allomorphs, 260, 265 contact position, 352

distance position, 352 final position, 353 inflectional paradigm, 262, 275 initial position, 360 second position, 351 sentential bracket (Satzklammer),

353 valency carrier, 304 valency positions, 349

verbal proplet, 456 Verbmobil, 24 verification, 6, 7, 18, 21, 379 view-dependent representation, 496 views (in knowlegde bases), 435 virtual reality, 16

washing machine, 14 weak equivalence, 167, 168, 208 WH-question, 489 what to say, 41, 99, 473, 478 word, 243, 244 word bank, 9, 437,439, 455,468,478,

483,517 word form, 21,241, 243,478 word form method, 254, 256 word form recognition, 37, 251, 253,

256 word order, 195, 197, 303, 457, 501

English, 316 German, 310 violation, 311, 352, 353

worst case, 132, 144 writing system, 118 WWW,21 WYSIWYG,29

XML (subset of SGML), 28

yes/no-question, 489

zero allomorph, 250 zero element (violating compositional­

ity), 418 Zipf's law, 295