COMPUTATIONAL APPROACHES TO REFERENCE

Jeanette Gundel &Massimo Poesio

LSA Summer Institute

Massimo Poesio:

Good morning. In this presentation I am going to summarize my research interests, at the intersection of cognition, computation, and language

Massimo Poesio:

Good morning. In this presentation I am going to summarize my research interests, at the intersection of cognition, computation, and language

The syllabus

Week 1: Linguistic and psychologic evidence concerning referring expressions and their interpretation

Week 2: The interpretation of pronouns Week 3: Definite descriptions and

demonstratives

Today

Linguistic evidence about demonstratives (Jeanette)

Poesio and Modjeska: Corpus-based verification of the THIS-NP Hypothesis

The Byron algorithm

RESOLVING DEMONSTRATIVES: THE PHORA ALGORITHM

LSA Summer Institute

July 17th, 2003

Massimo Poesio:

Good morning. In this presentation I am going to summarize my research interests, at the intersection of cognition, computation, and language

Massimo Poesio:

Good morning. In this presentation I am going to summarize my research interests, at the intersection of cognition, computation, and language

This lecture

Pronouns and demonstratives are different Pronoun resolution algorithms for

demonstratives PHORA Evaluation

Pronouns vs. demonstratives, recap of the facts

Put the apple on the napkin and then move it to the side.

Put the apple on the napkin and then move that to the side.

John thought about {becoming a bum}.

It would hurt his mother and it would make his father furious.

It would hurt his mother and that would make his father furious. (Schuster, 1988)

`Uniform’ pronoun resolution algorithms: LRC (Tetreault, 2001)

It They Them PRO Total

That This Those DEM total

Total

A Total number 89 19 10 118 127 2 18 147 265

B Expletives 32 0 0 32 2 0 0 2 34

C Det / rel pro 0 0 0 0 25 1 8 14 0

D Non-ref (B+C)

32 0 0 32 27 1 8 36 68

E Total ref (A-D)

57 19 10 86 100 1 10 111 186

G Abandon utt 7 0 0 7 7 0 4 11 18

I Eval set 50 19 10 79 93 1 6 100 179

LRC 27 16 8 51 (65%)

10 0 4 14(14%)

65 (36%)

PHORA (Byron, 2002)

Resolves both personal and demonstrative pronouns

Using SEPARATE algorithms (cfr. Sidner, 1979)

The Discourse Model, I: Mentioned Entities and Activated Entities

Discourse model contains TWO separated lists of objects

MENTIONED ENTITIES: interpretation of NPs– One for each referring expression: proper names, descriptive

NPs, demonstrative pronouns, 3rd person personal, possessive, and reflexive

ACTIVATED ENTITIES: – Each clause may evoke more than one PROXY for linguistic

constituents other than NPs– REFERRING FUNCTIONS are used to extract interpretation

The Discourse Model, II

Mentioned DEs remain in DM for the entire discourse

Activated Des updated after every CLAUSE FOCUS is the first-mentioned entity of a

clause; updated after each clause

Examples of Proxies

Infinitival / gerund TO LOAD THE BOXCARS takes one hour

Entire clause ENGINE E3 IS AT CORNING

Complement THAT-clause I think THAT ENGINE E3 IS AT CORNING

Subordinate clause If ENGINE E3 IS AT CORNING, we should send it to Bath.

Outline of the algorithm

1. Build discourse proxies for Discourse Unit n (DUn)

2. For each pronoun p in DUn+1

a. Calculate the MOST GENERAL SEMANTIC TYPE T that satisfies the constraints on the predicate argument position the pronoun occurs in

b. Find a referent that matches p using different search orders for personal and demonstrative pronouns

The importance of semantic context

Often the predicate imposes strong constraints on the type of objects that may serve as antecedents of the pronoun

Eckert and Strube (2000): I-INCOMPATIBLE vs. A-INCOMPATIBLE– That’s right– Let me help you lifting THAT

Semantic Constraints in PHORA

VERB’s SELECTIONAL RESTRICTIONS:– Load THEM into the boxcar CARGO(X)

PREDICATE NPs: force same type interpret.– That’s a good route ROUTE(X)

PREDICATE ADJECTIVES:– It’s right CORRECT(X) PROPOSITION(X)

NO CONSTRAINT:– That’s good ACCEPTABLE(X)

Searching for antecedents

Choose as antecedent the first DE that satisfies agreement features and semantic constraints for the pronoun, searching in different orders for personal and demonstrative pronouns.

Search: Personal pronouns

1. Mentioned entities to the left of the pronoun in the current clause, DUn+1, right-to-left

2. The focused entity of DUn

3. The remaining mentioned entities, going backwards one clause at a time, and then left-to-right in the clause

4. Activated entities in DUn

Search: Demonstrative pronouns

1. Activated entities in DUn

2. The focused entity of Dun (only if it can be coerced into a kind)

3. The remaining mentioned entities, going backwards one clause at a time, and then left-to-right in the clause

Referring Functions (Nunberg 1979, Webber 1988/1990)

IDENT The train is at Avon. Let’s send it to Bath.

KIND I finally bought a PDA. These gizmos …

SITUATION The train is at Avon. That’s not what I expected ….

EVENT The train arrived at Avon. I saw it happen.

KIND (A/E) Loading them takes an hour, and it must be done before the other train arrives

PROPOSITION S: The train is at Avon. M: That’s not true. It’s ..

ENDTIME We finished loading the boxcar. That was at 6pm.

Example, G0

G0: Engine 1 goes to Avon to get the oranges.

Engine 1 sing ENGINE indiv ENG1 FOCUS

Avon sing CITY indiv AVON MENTIONED

the oranges plur ORANGE indiv ORANGES1 MENTIONED

to get oranges

sing Functional kind Proxy ACTIVATED

all of G0 sing Functional indiv proxy ACTIVATED

the plan sing Functional indiv proxy ACTIVATED

Example, G1a, I

G1a: So it’ll get there at 3pm.

LF: (ARRIVE :theme x :dest y :time z)SEM CONSTRAINTS: MOVABLE-OBJECT(X)CANDIDATES: ENG1, ORANGESSEARCH: ENG1 produced first

Example, G1a, II – Discourse Model update

It sing ENGINE indiv ENG1 FOCUS

there sing CITY indiv AVON MENTIONED

3pm sing TIME indiv 3pm MENTIONED

all of G1a sing Functional indiv proxy ACTIVATED

the plan sing Functional indiv proxy ACTIVATED

Example, G1b

G1b: that takes two hours.

LF: (TAKE-TIME :theme x :cost y)SEM CONSTRAINTS: EVENT(X)SEARCH: Try first ACTIVATED entities of G1a, using referring function event(d)event(‘All of G1a’) is defined search succeeds

Example, G1c

G1c: that’s where the orange warehouse is.

LF: (EQUAL :theme x :complement y)SEM CONSTRAINTS: LOCATION(X)SEARCH: 1. ACTIVATED entities of G1b: no location 2. MENTIONED entities of G1b: nothing 3. Mentioned entities of G1a: AVON satisfies constraint search succeeds

Evaluation, I

It They Them PRO Total

That This Those

DEM total

Total

A Total number 89 19 10 118 127 2 18 147 265

B Expletives 32 0 0 32 2 0 0 2 34

C Det / rel pro 0 0 0 0 25 1 8 14 0

D Non-ref (B+C)

32 0 0 32 27 1 8 36 68

E Total ref (A-D)

57 19 10 86 100 1 10 111 186

G Abandon utt 7 0 0 7 7 0 4 11 18

I Eval set 50 19 10 79 93 1 6 100 179

LRC 27 16 8 51 (65%) 10 0 4 14 (14%) 65 (37%)

PHORA 37 15 10 62 (78%) 62 0 5 67 (67%) 129

(72%)

Evaluation, II

It They Them PRO Total

That This Those

DEM total

Total

I Eval set 50 19 10 79 93 1 6 100 179

LRC 27 16 8 51 (65%) 10 0 4 14 (14%) 65 (37%)

LRC + semantic constraint

32 14 11 57 14 0 5 19 77 (43%)

+ abstract referents

37 15 11 63 51 0 5 56 120 (67%)

+ different search (PHORA)

37 15 10 62 (78%) 62 0 5 67 (67%) 129

(72%)

References

M. Eckert and M. Strube .2000. Dialogue Acts, synchronizing units, and anaphora resolution. Journal of Semantics, 17(1).

B. Webber. Structure and ostension in the interpretation of deixis. Language and Cognitive Processes, 1990.