Lexical Knowledge and E-HowNet-2010-Proseminar II
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Transcript of 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
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Outline What is “understanding”?
Conceptual processing vs. string processing
Lexical Knowledge Representation
Semantic Composition andDecomposition
E-HowNet
Future researches
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What is “understanding”?
Conceptual Processing vs. String
ProcessingWhat is natural language understanding?
Why is conceptual processing so hard?
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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
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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.
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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
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A lexical knowledge representationsystems with semantic composition and
decomposition capabilities is the firststep toward conceptual processing andunderstanding by computers.
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Lexical KnowledgeRepresentation
Lexical knowledge
Early worksE-HowNet
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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 : …
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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. …
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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.
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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
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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
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Examples :
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HowNet ontology :
…
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Common sense knowledge
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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={?}}
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Outline
What is E-HowNet?
Lexical sense representation
Compositional semantics
Applications of E-HowNet
Future research
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What is E-HowNet?
Lexical sense representation
Compositional semantics
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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.
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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}}
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E-HowNet- SenseRepresentation
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 }
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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}
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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}}
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E-HowNet- SenseRepresentation
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.
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Principles for word sensedefinitions
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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.
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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={~}}}
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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)}
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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}}
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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}}))}}
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Spatial Concepts
Location adverbs
一路上 throughout the journey
def: LocationThru={route|道路}
到處 everywhere
def: location={all|全}
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Question Words
哪兒、哪裡 where
def: location={Ques()}
哪裡漏水?Where is leaking?
def:{
漏|leak:theme={
水},
location={Ques()}}
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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}}
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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})
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Unification of RelatedExpressions
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}}}
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Unification of RelatedExpressions
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}
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Semantic composition
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
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Semantic composition
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
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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={~}},
gender={female|女}},
manner={nimble|捷 }}
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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
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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
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Outline
What is E-HowNet?
Lexical sense representation
Compositional semantics
Applications of E-HowNet
Future research
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Applications of E-HowNet
• 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
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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))
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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}}
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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.
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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}
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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
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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}
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Object or Value+Act (nominal
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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}
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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
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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
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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.
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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}.
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Advantages of E-HowNet
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={~}},
gender={female|女}},
manner={nimble|捷 }}
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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
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Difficulties and future research
Semantic composition
Word identification- word segmentationand unknown word identification
Sentence parsing- syntactic structureanalysis and semantic role assignment
Word sense disambiguation
Meaning facet determination Generic or instance
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Difficulties and future research
Semantic composition
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