lecture 20: lexical relations & wordnet

2002.11.07 - SLIDE 1IS 202 – FALL 2002

Lecture 20: Lexical Relations & WordNet

Prof. Ray Larson & Prof. Marc DavisUC Berkeley SIMS

Tuesday and Thursday 10:30 am - 12:00 pmFall 2002

http://www.sims.berkeley.edu/academics/courses/is202/f02/

SIMS 202: Information Organization

and Retrieval

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Lecture Overview• Review

– Probabilistic Models of IR– Relevance Feedback

• Lexical Relations• WordNet• Can Lexical and Semantic Relations be

Exploited to Improve IR?

Credit for some of the slides in this lecture goes to Marti Hearst and Warren Sack

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Probability Ranking Principle• If a reference retrieval system’s response to

each request is a ranking of the documents in the collections in the order of decreasing probability of usefulness to the user who submitted the request, where the probabilities are estimated as accurately as possible on the basis of whatever data has been made available to the system for this purpose, then the overall effectiveness of the system to its users will be the best that is obtainable on the basis of that data.

Stephen E. Robertson, J. Documentation 1977

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Probabilistic Models: Some Unifying Notation

• D = All present and future documents• Q = All present and future queries• (Di,Qj) = A document query pair• x = class of similar documents, • y = class of similar queries, • Relevance (R) is a relation:

}Q submittinguser by therelevant judged

isDdocument ,Q ,D | )Q,{(D R

j

ijiji QD

Dx Qy

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Probabilistic Models• Model 1 -- Probabilistic Indexing, P(R|

y,Di)• Model 2 -- Probabilistic Querying, P(R|

Qj,x)

• Model 3 -- Merged Model, P(R| Qj, Di)• Model 0 -- P(R|y,x)• Probabilities are estimated based on prior

usage or relevance estimation

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Probabilistic ModelsQD

x

y

Di

Qj

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Logistic Regression• Another approach to estimating probability

of relevance• Based on work by William Cooper, Fred

Gey and Daniel Dabney• Builds a regression model for relevance

prediction based on a set of training data• Uses less restrictive independence

assumptions than Model 2– Linked Dependence

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Logistic Regression100 -90 -80 -70 -60 -50 -40 -30 -20 -10 -0 - 0 10 20 30 40 50 60

Term Frequency in Document

Rel

evan

ce

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Logistic Regression• Probability of relevance is based on Logistic

regression from a sample set of documents to determine values of the coefficients

• At retrieval the probability estimate is obtained by:

• For the 6 X attribute measures shown previously

6

10),|(

iii XccDQRP

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Relevance Feedback in an IR System

Interest profiles& Queries

Documents & data

Rules of the game =Rules for subject indexing +

Thesaurus (which consists of

Lead-InVocabulary

andIndexing

Language

StorageLine

Potentially Relevant

Documents

Comparison/Matching

Store1: Profiles/Search requests

Store2: Documentrepresentations

Indexing (Descriptive and

Subject)

Formulating query in terms of

descriptors

Storage of profiles Storage of

Documents

Information Storage and Retrieval System

Selected relevant docs

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Relevance Feedback• Main Idea:

– Modify existing query based on relevance judgements

• Extract terms from relevant documents and add them to the query

• And/or re-weight the terms already in the query– Two main approaches:

• Automatic (pseudo-relevance feedback)• Users select relevant documents

– Users/system select terms from an automatically-generated list

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Rocchio/Vector Illustration

Retrieval

Information

0.5

1.0

0 0.5 1.0

D1

D2

Q0

Q’

Q”

Q0 = retrieval of information = (0.7,0.3)D1 = information science = (0.2,0.8)D2 = retrieval systems = (0.9,0.1)

Q’ = ½*Q0+ ½ * D1 = (0.45,0.55)Q” = ½*Q0+ ½ * D2 = (0.80,0.20)

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Alternative Notions of Relevance Feedback

• Find people whose taste is “similar” to yours– Will you like what they like?

• Follow a users’ actions in the background– Can this be used to predict what the user will

want to see next?• Track what lots of people are doing

– Does this implicitly indicate what they think is good and not good?

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Alternative Notions of Relevance Feedback

• Several different criteria to consider:– Implicit vs. Explicit judgements – Individual vs. Group judgements– Standing vs. Dynamic topics– Similarity of the items being judged vs.

similarity of the judges themselves

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Syntax• The syntax of a language

is to be understood as a set of rules which accounts for the distribution of word forms throughout the sentences of a language

• These rules codify permissible combinations of classes of word forms

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Semantics• Semantics is the study of linguistic

meaning• Two standard approaches to lexical

semantics (cf., sentential semantics; and, logical semantics):– (1) compositional– (2) relational

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Lexical Semantics: Compositional Approach

• Compositional lexical semantics, introduced by Katz & Fodor (1963), analyzes the meaning of a word in much the same way a sentence is analyzed into semantic components. The semantic components of a word are not themselves considered to be words, but are abstract elements (semantic atoms) postulated in order to describe word meanings (semantic molecules) and to explain the semantic relations between words. For example, the representation of bachelor might be ANIMATE and HUMAN and MALE and ADULT and NEVER MARRIED. The representation of man might be ANIMATE and HUMAN and MALE and ADULT; because all the semantic components of man are included in the semantic components of bachelor, it can be inferred that bachelor man. In addition, there are implicational rules between semantic components, e.g. HUMAN ANIMATE, which also look very much like meaning postulates.– George Miller, “On Knowing a Word,” 1999

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Lexical Semantics: Relational Approach

• Relational lexical semantics was first introduced by Carnap (1956) in the form of meaning postulates, where each postulate stated a semantic relation between words. A meaning postulate might look something like dog animal (if x is a dog then x is an animal) or, adding logical constants, bachelor man and never married [if x is a bachelor then x is a man and not(x has married)] or tall not short [if x is tall then not(x is short)]. The meaning of a word was given, roughly, by the set of all meaning postulates in which it occurs.– George Miller, “On Knowing a Word,” 1999

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Pragmatics• Deals with the relation between signs or linguistic

expressions and their users• Deixis (literally “pointing out”)

– E.g., “I’ll be back in an hour” depends upon the time of the utterance

• Conversational implicature– A: “Can you tell me the time?”– B: “Well, the milkman has come.” [I don’t know exactly, but

perhaps you can deduce it from some extra information I give you.]

• Presupposition– “Are you still such a bad driver?”

• Speech acts– Constatives vs. performatives– E.g., “I second the motion.”

• Conversational structure– E.g., turn-taking rules

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Language• Language only hints at meaning• Most meaning of text lies within our minds

and common understanding– “How much is that doggy in the window?”

• How much: social system of barter and trade (not the size of the dog)

• “doggy” implies childlike, plaintive, probably cannot do the purchasing on their own

• “in the window” implies behind a store window, not really inside a window, requires notion of window shopping

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Semantics: The Meaning of Symbols

• Semantics versus Syntax– add(3,4)– 3 + 4– (different syntax, same meaning)

• Meaning versus Representation– What a person’s name is versus who they are

• A rose by any other name...– What the computer program “looks like”

versus what it actually does

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Semantics• Semantics: Assigning meanings to

symbols and expressions– Usually involves defining:

• Objects• Properties of objects• Relations between objects

– More detailed versions include • Events• Time• Places• Measurements (quantities)

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The Role of Context• The concept associated with the symbol

“21” means different things in different contexts– Examples?

• The question “Is there any salt?”– Asked of a waiter at a restaurant– Asked of an environmental scientist at work

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What’s In a Sentence? “A sentence is not a verbal snapshot or movie

of an event. In framing an utterance, you have to abstract away from everything you know, or can picture, about a situation, and present a schematic version which conveys the essentials. In terms of grammatical marking, there is not enough time in the speech situation for any language to allow for the marking of everything which could possibly be significant to the message.”

Dan Slobin, in Language Acquisition: The state of the art, 1982

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Lexical Relations• Conceptual relations link concepts

– Goal of Artificial Intelligence• Lexical relations link words

– Goal of Linguistics

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Major Lexical Relations• Synonymy• Polysemy• Metonymy• Hyponymy/Hyperonymy• Meronymy• Antonymy

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Synonymy• Different ways of expressing related concepts• Examples

– cat, feline, Siamese cat• Overlaps with basic and subordinate levels• Synonyms are almost never truly substitutable:

– Used in different contexts– Have different implications

• This is a point of contention

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Polysemy• Most words have more than one sense

– Homonym: same word, different meaning• bank (river)• bank (financial)

– Polysemy: different senses of same word• That dog has floppy ears.• She has a good ear for jazz.• bank (financial) has several related senses

– the building, the institution, the notion of where money is stored

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Metonymy• Use one aspect of something to stand for

the whole– The building stands for the institution of the

bank.– Newscast: “The White House released new

figures today.”– Waitperson: “The ham sandwich spilled his

drink.”

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Hyponymy/Hyperonymy• ISA relation• Related to Superordinate and Subordinate

level categories– hyponym(robin,bird)– hyponym(bird,animal)– hyponym(emu,bird)

• A is a hypernym of B if B is a type of A• A is a hyponym of B if A is a type of B

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Basic-Level Categories (review)• Brown 1958, 1965, Berlin et al., 1972, 1973• Folk biology:

– Unique beginner: plant, animal– Life form: tree, bush, flower– Generic name: pine, oak, maple, elm– Specific name: Ponderosa pine, white pine– Varietal name: Western Ponderosa pine

• No overlap between levels• Level 3 is basic

– Corresponds to genus– Folk biological categories correspond accurately to

scientific biological categories only at the basic level

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Psychologically Primary Levels

SUPERORDINATE animal furnitureBASIC LEVEL dog chairSUBORDINATE terrier rocker

• Children take longer to learn superordinate• Superordinate not associated with mental

images or motor actions

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Meronymy• Parts-of relation

– part of(beak, bird)– part of(bark, tree)

• Transitive conceptually but not lexically:– The knob is a part of the door.– The door is a part of the house.– ? The knob is a part of the house ?

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Antonymy• Lexical opposites

– antonym(large, small)– antonym(big, small)– antonym(big, little)– but not large, little

• Many antonymous relations can be reliably detected by looking for statistical correlations in large text collections. (Justeson &Katz 91)

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Thesauri and Lexical Relations• Polysemy: Same word, different senses of

meaning– Slightly different concepts expressed similarly

• Synonyms: Different words, related senses of meanings– Different ways to express similar concepts

• Thesauri help draw all these together• Thesauri also commonly define a set of relations

between terms that is similar to lexical relations– BT, NT, RT

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What is an Ontology?• From Merriam-Webster’s Collegiate:

– A branch of metaphysics concerned with the nature and relations of being

– A particular theory about the nature of being or the kinds of existence

• More prosaically:– A carving up of the world’s meanings– Determine what things exist, but not how they inter-

relate• Related terms:

– Taxonomy, dictionary, category structure• Commonly used now in CS literature to describe

structures that function as Thesauri

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WordNet• Started in 1985 by George Miller, students, and

colleagues at the Cognitive Science Laboratory, Princeton University

• Can be downloaded for free:– www.cogsci.princeton.edu/~wn/

• “In terms of coverage, WordNet’s goals differ little from those of a good standard college-level dictionary, and the semantics of WordNet is based on the notion of word sense that lexicographers have traditionally used in writing dictionaries. It is in the organization of that information that WordNet aspires to innovation.”– (Miller, 1998, Chapter 1)

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Presuppositions of WordNet project

• Separability hypothesis: T– The lexical component of language can be

separated and studied in its own right• Patterning hypothesis:

– People have knowledge of the systematic patterns and relations between word meanings

• Comprehensiveness hypothesis: – Computational linguistics programs need a

store of lexical knowledge that is as extensive as that which people have

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WordNet: Size

POS Unique Synsets Strings

Noun 107930 74488 Verb 10806 12754 Adjective 21365 18523 Adverb 4583 3612 Totals 144684 109377

WordNet Uses “Synsets” – sets of synonymous terms

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Structure of WordNet

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Unique Beginners• Entity, something

– (anything having existence (living or nonliving)) • Psychological_feature

– (a feature of the mental life of a living organism) • Abstraction

– (a general concept formed by extracting common features from specific examples)

• State– (the way something is with respect to its main

attributes; "the current state of knowledge"; "his state of health"; "in a weak financial state")

• Event– (something that happens at a given place and time)

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Unique Beginners• Act, human_action, human_activity

– (something that people do or cause to happen)• Group, grouping

– (any number of entities (members) considered as a unit)

• Possession– (anything owned or possessed)

• Phenomenon– (any state or process known through the senses

rather than by intuition or reasoning)

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WordNet Usage• Available online (from Unix) if you wish to

try it…– Login to irony and type “wn word” for any

word you are interested in– Demo…

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Lexical Relations and IR• Recall that most IR research has primarily

looked at statistical approaches to inferring the topicality or meaning of documents

• I.e., Statistics imply Semantics– Is this really true or correct?

• How has (or might) WordNet be used to provide more functionality in searching?

• What about other thesauri, classification schemes and ontologies?

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Natural Language Processing and IR

• The main approach in applying NLP to IR has been to attempt to address– Phrase usage vs individual terms– Search expansion using related

terms/concepts– Attempts to automatically exploit or assign

controlled vocabularies

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NLP and IR• Early research showed that (at least in the

restricted test databases tested)– Indexing documents by individual terms

corresponding to words and word stems produces retrieval results at least as good as when indexes use controlled vocabularies (whether applied manually or automatically)

– Constructing phrases or “pre-coordinated” terms provides only marginal and inconsistent improvements

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NLP and IR• Not clear why intuitively plausible

improvements to document representation have had little effect on retrieval results when compared to statistical methods– E.g. Use of syntactic role relations between

terms has shown no improvement in performance over “bag of words” approaches

– Semantics is even harder to accomplish• WordNet alone can’t disambiguate word senses in

texts

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Using NLP• Strzalkowski

Text NLP repres Dbasesearch

TAGGERNLP: PARSER TERMS

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Using NLP

INPUT SENTENCEThe former Soviet President has been a local hero ever since a Russian tank invaded Wisconsin.

TAGGED SENTENCEThe/dt former/jj Soviet/jj President/nn has/vbz been/vbn a/dt local/jj hero/nn ever/rb since/in a/dt Russian/jj tank/nn invaded/vbd Wisconsin/np ./per

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Using NLP

TAGGED & STEMMED SENTENCEthe/dt former/jj soviet/jj president/nn have/vbz be/vbn a/dt local/jj hero/nn ever/rb since/in a/dt russian/jj tank/nn invade/vbd wisconsin/np ./per

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Using NLPPARSED SENTENCE[assert [[perf [have]][[verb[BE]] [subject [np[n PRESIDENT][t_pos THE] [adj[FORMER]][adj[SOVIET]]]] [adv EVER] [sub_ord[SINCE [[verb[INVADE]] [subject [np [n TANK][t_pos A] [adj [RUSSIAN]]]] [object [np [name [WISCONSIN]]]]]]]]]

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Using NLP

EXTRACTED TERMS & WEIGHTSPresident 2.623519 soviet 5.416102President+soviet 11.556747 president+former 14.594883Hero 7.896426 hero+local 14.314775Invade 8.435012 tank 6.848128Tank+invade 17.402237 tank+russian 16.030809Russian 7.383342 wisconsin 7.785689

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NLP & IR Research Issues• Is natural language indexing using more

NLP knowledge needed?• Or, should controlled vocabularies be used• Can NLP in its current state provide the

improvements needed• How to test

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NLP & IR Research Areas• Lewis and Sparck Jones (CACM 1996) suggest

research in three areas– Examination of the words, phrases and sentences

that make up a document description and express the combinatory, syntagmatic relations between single terms

– The classificatory structure over document collection as a whole, indicating the paradigmatic relations between terms and permitting controlled vocabulary indexing and searching

– Using NLP-based methods for searching and matching

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NLP & IR: Possible Approaches

• Indexing– Use of NLP methods to identify phrases

• Test weighting schemes for phrases– Use of more sophisticated morphological

analysis • Searching

– Use of two-stage retrieval • Statistical retrieval• Followed by more sophisticated NLP filtering

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Can Statistics approach Semantics?

• One approach is the Entry Vocabulary Index (EVI) work being done here…

• (The following slides are from my presentation at JCDL 2002)

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What is an Entry Vocabulary Index?

• EVIs are a means of mapping from user’s vocabulary to the controlled vocabulary of a collection of documents…

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Start with a collection of documents.

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Classify and index with controlled

vocabulary.Index

Ideally, use a database

already indexed

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Problem:Controlled

Vocabularies can be difficult

for people to use.“pass mtr

veh spark ign eng”

Index

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Solution:Entry Level Vocabulary

Indexes.Index

EVIpass mtr veh

spark ign eng”

= “Automobile”

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EVI example

EVI 1

Index term:“pass mtr veh spark ign eng”User

Query “Automobile

” EVI 2Index term:“automobiles”OR

“internal combustible engines”

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But why stop there?

Index

EVI

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“Which EVI do I use?”

Index

EVI

Index

Index EVI

IndexEVI

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EVI to EVIs

Index

EVI

Index

Index EVI

IndexEVI

EVI2

2002.11.07 - SLIDE 72IS 202 – FALL 2002

FindPlutonium

In Arabic Chinese Greek Japanese Korean Russian Tamil

Why not treat language the same way?

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FindPlutonium

In Arabic Chinese Greek Japanese Korean Russian Tamil

...),,2[logL(p t)W(c, 1 baaStatistical association

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