Lexical Knowledge and E-HowNet-2010-Proseminar II

8/6/2019 Lexical Knowledge and E-HowNet-2010-Proseminar II

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E-HowNet- a Lexical KnowledgeRepresentation System for Semantic

Composition

Keh-Jiann Chen

CKIP

Institute of Information Science Academia Sinica



Outline What is “understanding”?

Conceptual processing vs. string processing

Lexical Knowledge Representation

Semantic Composition andDecomposition

E-HowNet

Future researches



What is “understanding”?

Conceptual Processing vs. String

ProcessingWhat is natural language understanding?

Why is conceptual processing so hard?



Conceptual Processing vs.

String Processing Information retrieval

E.g. retrieve “土地公” 福德正神 vs. 土地公有、土地公開

Word segmentation 土地公開買賣。土地公開買賣。土地公開罵。土地公開罵。

Understanding 土地公開罵福德正神很生氣



What is natural language

understanding? For each word, phrase, or sentence,

there is a way to derive its canonical

meaning representation?

From the meaning representation theassociated information and their

relations can be accessed.



Why is conceptual processing

so hard? Ambiguities

Speechtextconcept

Semantic opaqueness Lexicalized concepts:白宮、紅火蟻 Construction meaning and ellipses:我大你三歲

我的年紀比你的年紀大三歲 Metaphors:兩軍廝殺激烈。(Sport? Chess?)

Background knowledge: 雞兔同籠



A lexical knowledge representationsystems with semantic composition and

decomposition capabilities is the firststep toward conceptual processing andunderstanding by computers.



Lexical KnowledgeRepresentation

Lexical knowledge

Early worksE-HowNet



Lexical knowledge representation Words : the smallest meaningful units of a

language serve as indices to access various

knowledge. Word : sense1 : grammatical functions

semantic knowledgeworld knowledge

sense2 : …

sense3 : …



Granularity of Sense distinction

打 :1. play ball1.1 打籃球 1.2

打棒球 1.3 …

2. dial

(打電話；通電話；撥電話；打手機；打大哥大；通話‧‧‧)

3. beat

4. …



Sense Representation

WordNet approach :

A synset is a set of words with the same part-of-speech and refer to the same concept.

A synset is described by a gloss.

“ 4-wheeled; usually propelled by an internalcombustion engine”.

Synsets can be related to each other by semanticrelations.



Example:

Synset :{car; auto; automobile; motocar}

{vehicle}

{conveyance; transport}

{car; auto; automobile; machine; motorcar}

{cruiser; squad car; patrol car; police car; prowl car} {cab; taxi; hack; taxicab; }

{motor vehicle; automotive vehicle}

{bumper}

{car door}

{car window}

{car mirror}

{hinge; flexible joint}

{doorlock}

{armrest}

hyperonym

hyperonym

hyperonym

hyperonymhyperonym

meronym

meronym

meronym

meronym



Table 1: WordNet1.5 Relations

Relation PoS linked Example EWN

ANTONYMY noun/noun; verb/verb;

adjective/adjective

man/woman; enter/exit;

beautiful/ugly

yes

HYPONYMY noun/noun slicer/knife yes

MERONYMY noun/noun head/nose yes

ENTAILMENT verb/verb buy/pay SUBEVENT or

CAUSE

TROPONYM verb/verb walk/move HYPONYMY

CAUSE verb/verb kill/die yes

ALSO SEE verb/adjective no

DERIVED FROM adjective/adverb beautiful/beautifully yes

ANTONYM noun/noun; verb/verb heavy/light yes

ATTRIBUTE noun/adjective size/small XPOS_HYPONYM

RELATIONAL

ADJ

adjective/noun atomic/ atomic bomb PERTAINS TO

SIMILAR TO adjective/adjective ponderous/heavy no

PARTICIPLE adjective/verb elapsed/ elapse no







Examples :



HowNet ontology :

…



Common sense knowledge



Disadvantages

Representation by primitives degrades precision andreadability. 老虎 tiger DEF={beast|走獸 }

鉗子forceps DEF={tool|用具:{hold|拿:instrument={~}}} 鐘錶店 watchmaker's shop

DEF={InstitutePlace|場所 :{buy|買:location={~},possession={tool|用具:{tell|告訴 :content={time|時間},instrument={~}}}},{repair|修理:location={~},patient={tool|用具:{tell|告訴 :content={time|

時間},instrument={~}}}},{sell|

賣:

location={~},possession={tool|用具:{tell|告訴 :content={time|時間},instrument={~}}}}}

Without considering semantic composition anddecomposition. E.g. function words: 僅 just

DEF={FuncWord|功能詞:emphasis={?}}





Outline

What is E-HowNet?

Lexical sense representation

Compositional semantics

Applications of E-HowNet

Future research



What is E-HowNet?





E-HowNet E-HowNet is an entity-relation model

extended from HowNet for lexical semanticrepresentation.

A uniform semantic representation for functionwords, content words and phrases.

Semantic relations are explicitly expressed in E-HowNet representations.

Semantic composition and decompositioncapabilities.

Near-canonical sense representation.



E-HowNet- SenseRepresentation

Word sense definition- decompose a sense intosimpler senses and sense relations are explicitlyexpressed

果盤 fruit platedef:{plate|盤:telic={put|放置: location={~},patient={fruit|

水果}}}

玻璃盤 glass plate

def: {plate|盤:material={glass|玻璃}}

圓盤 round plate

def: {plate|盤:shape={round|圓}}




Uniform representation for functionwords, content words and phrases

Preposition: 從 • def: location-source={},

• def: time-init={}

Conjunction: 因為• def: reason={}

Adverb: 透頂• def: degree={very|很 }



Definitions of function wordsFunction words Content words

Relational senses ---------------------------------------- Content sensesDe的, prepositions, …adjectives, verbs, nouns

Conjunctions, adverbs…

Preposition: 從 from

• def: location-source={},

• def: time-init={}Conjunction: 因為 because

• def: reason={}

Adverb: 透頂 very• def: degree={very|很 }

Noun:果盤 fruit plate

• def:{plate|

盤:telic=

{put|放置: location={~},

patient={fruit|水果}}}

Verb:下雨 rain

• def: {rain|下雨}



Uniform representation

Preposition: 把 ba def: goal={}

Noun: 文章article def: {text|語文}

Verb: 寫好have written def: {write|寫:aspect aspect={Vachieve|達成}}

Phrase: 把文章寫好 The article has been

written . {write|寫:goal={text|語文}, aspect={Vachieve|達

成}}






High-level representations can be decomposedinto primitive representations. The primitives are adopted from HowNet, called

sememes 義原.

The set of primitives has about two thousand elementsand organized into taxonomy of entities and relations.





Principles for word sensedefinitions





Principles for sense definitions

Agentive-the factors involved in the origin or“bringing about” of the object

Telic-the purpose and function of the object

Constitutive-the relations between the object and itsconstituents, such as its materials, parts, andcomponents

Formal-the properties to distinguish the object withina larger domain, such as its shape, magnitude, andcolor etc.





Agentive

早產兒premature baby

def: {嬰兒|baby: agentive={早產:patient={~}}}

def: {human|人:age={child|少兒},agentive={labour|臨產:manner={early|早},

patient={~}}}



Formal

彩霞 rosy clouds

def: {CloudMist|雲霧 :color={colored|彩}}

酸辣湯spicy and sour soup def: {湯|soup:taste={and(酸 |sour, 辣|hot)}

def: {food|食品:material={Liquid|液},

taste={and(sour|酸 , peppery|辣)}



Constitutive

草裙grass skirt

def: {裙|skirt:material={草|grass}}

def: {clothing|衣物:telic={PutOn|穿戴:instrument={~},location={leg|腿 :whole={human|人:gender={female|女}}}},material={FlowerGrass|

花草}}







Functional compositions

姪女 niece

Def: {daughter(brother(x:human|

人))}

西北郊 north west suburb

def: {地方:position={north(west(郊|suburb))}}

def: {place|地方: position={north(west({edge|邊: whole={city|市}}))}}



Spatial Concepts

Location adverbs

一路上 throughout the journey

def: LocationThru={route|道路}

到處 everywhere

def: location={all|全}







Question Words

哪兒、哪裡 where

def: location={Ques()}

哪裡漏水？Where is leaking?

def:{

漏|leak:theme={

水},

location={Ques()}}







Semantic composition

E.g. 因為下雨，衣服都濕了Because of raining, clothes are all wet.

Parsing and semantic role labeling:

S(reason:VP(Head:Cb:因為|dummy:VA:下雨)|theme:NP(Head:Na:衣服) | quantity: Da:都 | Head:Vh:濕|particle:Ta:了)

E-HowNet lexical senses:因為 def: reason={}

下雨 def: {rain|下雨}衣服 def: {clothing|衣物}都 def: manner={complete|整 }濕 def: {wet|濕}了 def: aspect={Vachieve|達成}

unification

Semantic Composition:

def:{wet|濕:

theme={clothing|衣物},aspect={Vachieve|達成},

manner={complete|整 },

reason={rain|下雨}}



Unification of RelatedExpressions

Conjunctive relation

Exp1 and/or Exp2

Semantic composition:

and/or(Exp1, Exp2);

e.g.中美中|China and 美|America And({中|China}, {美|America})




Head-modifier or head-argument relations Need to identify semantic role of Exp1 if head is

Exp2. Semantic composition:

{Exp2: role={Exp1}}

e.g.好|good 學生|student

{學生|student: quality={好|good}} {human|人:quality={nice|良好},predication={study|學習

:agent={~},domain={education|教育}}}




Function word as syntactic head

Semantic composition:

rel1={Exp2} E.g. 從 |from 台北 |Taipei

Preposition: 從 from

def: location-source={place|地方

},

def: time-init={time|時間}

location-source={台北 |Taipei}




Processing technologies:

Identify senses of new compound words

Sense disambiguation

Syntactic parsing and semantic role assignment

Resolution of anaphoric references

Filling gaps

Process construction meaning and metaphoricinferences

Derive near-canonical conceptual representation




Achieves near canonical meaningrepresentation. vs.

Captain cleverly captured woman who is looting. Syntactic parsing

Def:{抓獲 :agent={ 機長}, patient={搶犯:gender={女}},manner={ 機敏 }}

Def:{逮捕

:agent={飛機駕駛員

},patient={強盜

:gender={女}}, manner={敏捷 }}

Semantic composition and decomposition



It is the first step toward machine understanding. Def: {catch|捉住:

agent={human|人:HostOf={Occupation|職位},modifier={official|官},

predication={manage|管理:agent={~},patient={aircraft|飛行器}}},patient={human|人:modifier={guilty|有罪 },predication={rob|搶 :agent={~}},

gender={female|女}},

manner={clever|靈 }}

Def: {catch|捉住:

agent={human|

人:HostOf={Occupation|

職位},modifier={official|

官},

predication={manage|管理:agent={~},patient={aircraft|飛行器}}},

patient={human|人:modifier={guilty|有罪 },predication={rob|搶 :agent={~}},


manner={nimble|捷 }}



Why is E-HowNet a near-canonical senserepresentation system?

Sense similarity can be measured throughtaxonomies for entities and relations

Functional composition and relational

identification Default value and feature inheritance Semantic decomposition Semantic composition by feature unification View point normalization Default reasoning



Current Status of E-HowNet

Coarse-grained sense representation

Sense representations for about 80,000

entries of CKIP dictionary

Taxonomy for entities (sememes) andrelations

Mapping between sememes andWordNet synsets



Outline

What is E-HowNet?




Future research




• Meaning representation• Conceptual processing

• Synonym generation

• Disambiguation• Specialization• Generalization（舉一反三）• Association (聯想)

• Inference (享受|enjoy 牛排 |steak/音樂|music)• Applications: IR, machine translation,

understanding, semantic analysis, informationprocessing at concept level









Machine Translation

e.g. 桌子上放了這本書。This book was put on the table.

Google translation:

Put the table on this book. S(location:NP(property:Nab:桌子|Head:Ncda:上)|

Head:VC33:放 |aspect:Di:了|

theme:NP(quantifier:DM:這本|Head:Nab:書))



Question answering

衣服上的墨水怎麼洗掉？How do you clean ink spots on clothes?

def:{wash|洗掉 :patient={ink|墨水:place={clothes|衣服}},means={Ques()}}

漂白水可以洗掉墨水。Bleach may clean ink spots. def:{wash|洗掉 :patient={ink|墨水},

means={漂白水|bleach}}



Four major semantic types

Semantic classification may cross posclassification.

Events (acts) vs. Verbs

Objects vs. Nouns

Attributes (relations) vs. Nouns

Values (values of attributes) vs. Stateverbs, nouns

Words of same semantic class holdparticular syntactic properties.





Values

Values associate with respectiveattributes and lead to identify semantic

relations. 紅|red 酒|wine 三兩|3 ounce 肉|meat

陳|old

酒|wine

快 |fast

車|car

color weight

Time Agentive={produce|製造}

SpeedTelic={move|移動}



Application of semantic types

Disambiguation of transitive Verb+Noun structure

verb objects modifier head

檢驗 |inspect + Noun

行李|luggage,食物|food vs. 制度|system,方法|method

Pos: Noun Pos: Noun

Semantic: objects Semantic: attribute









Obj1+Obj2

Obj1+Obj2Object Noun

More than 99% are modifier+ headstructure.

{Obj2: rel={Obj1}} where rel could beTelic, Agentive, material, part, location, ….

油井槍彈 Note: objects are rarely to be suffix or

prefix of verbs.

Telic={produce|製造} Predication={擊發}







Object or Value+Act (nominal



Object or Value+Act (nominalaction)

Obj or Val+Action affairs

Action={存、收、考、行、吻、射、改、治、防、…}{affairs|事務 : CoEvent={Act}}

Animal+{叫}

長、安、全壘+{打}



Obj+Value

Obj+Shape-Value(形狀量詞)

{Obj: shape={value}}

E.g. 串、粉、捲、圈、桿、棒、管、環、末、條、塊、團、屑 Obj+color-value color-Value

米白、酒紅 Obj+odor-value odor Noun

香、臭、腥



Value+Obj

Modifier+head object Noun

{object: Attribute={value}}

E.g. 紅花、富人、昏君 Very few exceptions:

沿路 adv, 炫人 verb



Act+Obj

Act+Obj Obj Noun /* It is differentfrom syntactic construction.*/

E.g. 炒飯、用水、烤肉、吊櫃 Some of examples have ambiguous

interpretations.

The acts playing the role of prefix of object nouns generally do not play therole of prefix of verbs.







Advantages of E-HowNet

Features are criteria for new classifications.Great dane|大丹狗 is also classified as：

1) Hunting instruments|狩獵工具：

firearm| 獵槍 def: {gun|槍 :telic={hunt|狩獵 :instrument={~}}}

trap|陷阱 def: {facility|設施 :telic={hunt|狩獵 :instrument={~}}}

2) Animals with black/white colors|黑白色系的動物： panda|熊貓 def: {beast|走獸 :place={China|中國}, predication={eat|吃:

patient={bamboo|竹子},agent={~}},color={黑白}} Zebra|斑馬 def: {horse|馬:color={黑白},size={small|小型}}

cf. HowNet definitions are very rough; for examples all dogs are

defined as: {livestock|牲畜}.





Advantages of E-HowNet

Def: {catch|捉住:


predication={manage|管理:agent={~},patient={aircraft|飛行器}}},



manner={clever|靈 }}

Def: {catch|捉住:


predication={manage|

管理:agent={~},patient={aircraft|

飛行器}}},



manner={nimble|捷 }}











Difficulties and future research

Semantic representation Domain specific concepts

Domain terms:質數

|prime number、二氧化碳 |carbon dioxide …

Relative entities:他人|others、外野| out field …

Fine-grained features

Aspects and viewpoints





Word identification- word segmentationand unknown word identification

Sentence parsing- syntactic structureanalysis and semantic role assignment

Word sense disambiguation

Meaning facet determination Generic or instance





Anaphoric references

Fine-grained semantic relations and gaps Construction meaning and metaphoric

inferences

View point normalization

Buy 買: Sell 賣 Borrow 借: Lend 借 Cause 因為: Result 所以

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Lexical Knowledge and E-HowNet-2010-Proseminar II