Introduction to Computational Linguisitics The Lexicon

Introduction An inventory of words is an essential component of programs for a wide variety of language sensitive applications, such as: –Spellchecking, stylechecking –IR, IE, message understanding –parsing, generation, MT –TTS and STT Such an inventory usually called a dictionary or lexicon.

Dictionaries The purpose of a dictionary is to provide a wide range of information about words Some of this is linguistic information, e.g. syntactic category, pronunciation, distribution. But dictionaries also contain definitions of word senses thus providing knowledge about not just language but about the world itself.

What is "dog"? dog (ANIMAL) [show phonetics] noun [C] a common four-legged animal, especially kept by people as a pet or to hunt or guard things: my pet dog wild dogs dog food We could hear dogs barking in the distance. (from Cambridge Advanced Learners Dictionary)

Senses of Dog dog was found in the Cambridge Advanced Learner's Dictionary at the entries listed below.Cambridge Advanced Learner's Dictionary –dog (ANIMAL)dog (ANIMAL) –dog (PERSON)dog (PERSON) –dog (FOLLOW)dog (FOLLOW) –dog (PROBLEM)dog (PROBLEM) different senses or lexemes for dog

Two Views of the Lexicon give rise to different issues Lexicon as word database –How to represent the word collection –Lookup: given an arbitrary word, how to access the relevant entries –What information to provide and how to express it. Lexicon as database about word senses –Representation of word sense –What are the relations between word senses? –How do word senses hook up with concept knowledge

Lexicon as Word Database: Representing the Word Collection Some possible representations: –Text file –Finite state automaton. –Other specialised data structure which allows for common prefixes, e.g. letter tree Full form vs. lexeme + morphological analysis

FSA for Sublexicon Fragment thes e i s a t o

Letter Tree ltree([ [b, [a, [r, [k, bark]]]], [c, [a, [r, [r, [y, carry]]], [t, cat, [e, [g, [o, [r, [y, category]]]]]]]], [d, [e, [l, [a, [y, delay]]]]], [h, [e, [l, [p, help]]], [o, [p, hop, [e, hope]]]], [q, [u, [a, [r, [r, [y, quarry]]]], [i, [z, quiz]], [o, [t, [e, quote]]]]] ]).

Full Form Dictionary There is an entry for every possible word. No need for morphological processing Exceptions are handled automatically OK when number of entries is not too large. Repeated information. Because languages have different morphological properties, full form is better for some languages than for others.

Morphological Analysis + Lexicon Morphological Analysis Input Word cats LEXICON catN sPL s3SG

Morphological Analysis Very roughly, morphological analysis of a word involves 2 subproblems: A segmentation problem: how to get from the written text to the sequence of morphemes that make it up. A morphotactic problem: how to combine the individual morphemes together in a legitimate way.

Segmentation/Morphotactic Subproblems Segmentation problem: –enlargement => en + large + ment Morphotactic problem: given what we know about en, large and ment, how can they be legitimately combined –enlargement => (en + large) + ment –enlargement =/> en + (large + ment) –en + ADJ => V –V + ment => N

2-Level Morphology In 1981 the four Ks (Kimmo Koskenniemi, Lauri Karttunen, Ronald M. Kaplan and Martin Kay) were working on morphological analysis (MA) Basic idea was that MA is about computing relation between sets of strings at two levels: –Surface Level (string of lexical words made from surface alphabet) –Lexical Level (string of morphemes made of lexical alphabet). Relation can be computed using finite state transducers. Reversibility of finite-state model

What Information to Provide Specific Information – eg "kicks" Syntactic Information –POS = verb –Tense = pres –Number = singular –Person = 3 –Type =Transitive Semantic Information –event-type = Physical Action –type-of subject = animate –type-of object = physical

What Information to Provide General Information Class Attributes –Agreement has (Number, Gender) Enumeration of possible values –Gender = [masc, fem] –Number = [sing, plur] Class Relationships –Transitive isa Verb –Common isa Noun

Two Views of the Lexicon give rise to different issues Lexicon as word database –How to represent the word collection –Access: given an arbitrary word, how to access the relevant entries –What information to provide and how to express it. Lexicon as database about word senses –What are the relations between word senses? –How do word senses hook up with conceptual knowledge

WordNet In 1985 a group of psychologists and linguists at Princeton had the idea of searching dictionaries conceptually rather than alphabetically. Attempt to organise a dictionary in terms of word meanings rather than word forms. What is the nature and organisation of the lexicalised concepts that words can express? Distinction between word forms, word meanings, and entries.

Lexical Matrix Word Meanings Word Forms F1F2..Fn M1E1,1E1,2 M2E2,1.. MmEm,n polysemy synonymy entries

WordNet A key aspect of WordNet is that a given meaning or word sense is represented as the set of words that can be used to express it. These meanings are called synsets – sets of words with synonymous readings. Synsets are established empirically according to a principle of substitutability that is relativised to context.

The Principle of Substitutability Two expressions are synonymous if the substitution of one for another never alters the truth value of a sentence in which the substitution is made. Two expressions are synonymous in linguistic context C if the substitution of one for the other in C does not alter the truth value. e.g. plank/board in carpentry contexts

Lexical Matrix Word Meanings Word Forms boardcommitteeplank..Fn board committee E1,1E1,2 board plank E2,1E2,3.. MmEm,n entries

WordNet In Wordnet, the synonymy relation between words is fundamental. Synsets can be thought of as representing concepts which stand in various semantic relations to each other. –X Antonym Y: meaning (synset) X is opposite to meaning (synset) Y (big, small) –X Hyponym Y: like isa (e.g. dog, mammal) –X Meronym Y: X is a part of Y (e.g. leg, man)

Lexicon as a Concept Graph We can thus imagine the WordNet Lexicon as a gigantic graph whose nodes are synsets and whose arcs are semantic relations between synsets. Such a structure can be regarded as a semantic map of the concepts used in a given language. Many applications can be created using the WordNet graph as a resource

Using WordNet to Measure Semantic Orientations of Adjectives Jaap Kamps, Maarten Marx, Robert J. Mokken, Maarten de Rijke

Conclusion Lexicon is a central building block of language- sensitive systems Schizophrenic status of lexical information: linguistic versus world knowledge. As a wordlist, lexicon has to solve problem of representation and access. Morphological analysis can help to keep number of entries to a manageable level. As a collection of definitions, lexicon has to deal with relationships between word meanings.