1 Word senses: a computational response Adam Kilgarriff Auckland 2012Kilgarriff: Word senses: a computational response

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Word senses: a computational response

Adam Kilgarriff

Auckland 2012 Kilgarriff: Word senses: a computational response

Auckland 2012 Kilgarriff: Word senses: a computational response 2

My PhD (in 5 slides)

What is a word sense


The lexicographers

They create them Methods

Introspection Other dictionaries Corpus

Atkins, Hanks, Krishnamurthy


What is a word sense (1)

SFIP Sufficiently frequent insufficiently

predictable (a glass of) whisky x (a glass of) tequila



homonymy

analogy polysemy rules

collocation



A cluster Of instances of use

Operationalised as: corpus lines Clustered by lexicographers











A cluster Of instances of use

Operationalised as: corpus lines Clustered by lexicographers

Makes sense of Overlapping senses Different dictionaries, different senses Lumping and splitting


I don’t believe in word senses

Believe in: resurrection ghost witch vampire god

miracle fairy Philosophy:

Ontological commitment (same meaning different register)

“good entities to build belief systems on”


A word sense is a cluster of corpus lines

But I’m an NLP person Automatic clustering? Inspiration:

Hindle 1991, Schütze 1993, Grefenstette 1993, Lin 1999

You can get semantic sense from corpora+stats


First attempt

Longman 1994 Abject failure

No grammar Corpus too small and noisy Naïve clustering Useless programmer


Collocations

Easy Most words don’t go with most other

words Then build on what we can do well metaphor, analogy, homonymy,

rules all much harder


Clustering

Word sketch Collocates organised by grammar

Dictionary Collocates (and other things) organised

by meaning How to re-organise


Observation:

corpus: arbitrary sample dictionary (=lexicon): systematic

account

Children encounter arbitrary samples develop systematic account


Corpus provisional, dispensable used to develop lexicon


Levels of abstraction

Direct linkage:

Fragile Updates (to C or D) break links

Dictionary: abstract Corpus: raw

Intermediate level needed

CorpusCorpus DictionaryDictionary

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How most automatic word sense disambiguation (WSD) works Analyse dictionary to give set of

collocates Match to collocates in a corpus

Dispensable corpus


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CollocatesCollocates


Not just collocates triples

<object, drink (v), tea (n)> parse the corpus some “unary relations”

<v+obj+ing, hear (v)> I hear him singing domain-based clues

<domain=computing, mouse (n)>

Collocates, Constructions, Domains = CoCoDo


Automatically extract CoCoDos from corpus How linked to senses?

Automatic (WSD techniques) Manual

“dictionary-free”: ideal for new dictionaries Labour costs

Mixed WSD with manual confirmation/correction


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CoCoDoCoCoDo

Linking CoCoDo’s to senses


Semi-automatic dictionary drafting (SADD)

CoCoDo database Automatic clustering Lexicographer input More clustering

Dictionary with corpus inside


Automatic clustering of collocates Propose senses

Iterate: Lexicographer input

Confirm/reject/edit sense inventory Assigns collocates / corpus lines to senses

WSD Uses seeds to build full WSD for word Find more collocates for each sense

XML dictionary entry Load into dictionary-editing tool


Fits with Atkins method for bilingual lexicography Analyse source language

From corpus List all expressions that might possibly have a

non-predictable translation Very fine grained Lots of collocations

target-language-neutral; re-usable Translate Edit to finalise dictionary

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1 Word senses: a computational response Adam Kilgarriff Auckland 2012Kilgarriff: Word senses: a computational response