Thesauruses for Natural Language Processing

Adam Kilgarriff

Lexicography MasterClass

and

University of Brighton

Outline

Definition Uses for NLP WASPS thesaurus web thesauruses Argument: words not word senses Evaluation proposals Cyborgs

What is a thesaurus?

a resource that groups words according to similarity

Manual and automatic

Manual– Roget, WordNets, many publishers

Automatic– Sparck Jones (1960s), Grefenstette (1994), Lin

(1998), Lee (1999) – aka distributional– two words are similar if they occur in same

contexts

Are they comparable?

Thesauruses in NLP

sparse data

Thesauruses in NLP sparse data

does x go with y?– don’t know, they have never been seen together

New question:does x+friends go with y+friends– indirect evidence for x and y– thesaurus tells us who friends are– “backing off”

Relevant in:

Parsing– PP-attachment– conjunction scope

Bridging anaphors Text cohesion Word sense disambiguation (WSD) Speech understanding Spelling correction

Speech understanding

He’s as headstrong as an alleg***** in the upwaters of the Yangtze

Speech understanding

He’s as headstrong as an alleg***** in the upwaters of the Yangtze

allegory?

Speech understanding

He’s as headstrong as an alleg***** in the upwaters of the Yangtze

allegory? alligator?

Speech understanding

He’s as headstrong as an alleg***** in the upwaters of the Yangtze

allegory? in upwaters? No alligator? in upwaters? No

Speech understanding

He’s as headstrong as an alleg***** in the upwaters of the Yangtze

allegory? in upwaters? No alligator? in upwaters? No allegory+friends in upwaters? No alligator+friends in upwaters? Yes

PP-attachmentinvestigate stromatolite with microscope/speckles

– microscope: verb attachment– speckles: noun attachment

inspect jasper with spectrometer– which?

PP attachment (cont)

compare frequencies of– <inspect, with, spectrometer>– <jasper, with, spectrometer>

PP attachment (cont)

compare frequencies of– <inspect, with, spectrometer>– <jasper, with, spectrometer>

both zero? Try– <inspect+friends, with,

spectrometer+friends>– <jasper+friends, with,

spectrometer+friends>

Conjunction scope

Compare– old boots and shoes– old boots and apples

Conjunction scope

Compare– old boots and shoes– old boots and apples

Are the shoes old?

Conjunction scope

Compare– old boots and shoes– old boots and apples

Are the shoes old? Are the apples old?

Conjunction scope

Compare– old boots and shoes– old boots and apples

Are the shoes old? Are the apples old? Hypothesis:

– wide scope only when words are similar

Conjunction scope

Compare– old boots and shoes– old boots and apples

Are the shoes old? Are the apples old? Hypothesis:

– wide scope only when words are similar hard problem: thesaurus might help

Bridging anaphor resolution

– Maria bought a large apple. The fruit was red and crisp.

fruit and apple co-refer

Bridging anaphor resolution

– Maria bought a large apple. The fruit was red and crisp.

fruit and apple co-refer How to find co-referring terms?

Text cohesion

words on same theme– same segment

change in theme of words– new segment

same theme: same thesaurus class

Word Sense Disambiguation (WSD) pike: fish or weapon

– We caught a pike this afternoon probably no direct evidence for

– catch pike probably is direct evidence for

– catch {pike,carp,bream,cod,haddock,…}

WordNet, Roget

widely used for all the above

The WASPS thesaurus– credit: David Tugwell– EPSRC grant K8931

POS-tag, lemmatise and parse the BNC (100M words)

Find all grammatical relations– <obj, climb, bank>– <modifier, big, bank>– <subject, bank, refuse>

70 million triples

WASPS thesaurus (cont)

Similarity:– <obj, drink, beer>– <obj, drink, wine>

one point similarity between beer and wine count all points of similarity between all pairs

of words weight according to frequencies

– product of MI: Lin (1998)

Word Sketches

one-page summary of a word’s grammatical and collocational behaviour

demo: http://wasps.itri.bton.ac.uk the Sketch Engine

– input any corpus– generate word sketches and thesaurus– just available now

Nearest neighbours to zebra

Nearest neighbours

zebra: giraffe buffalo hippopotamus rhinoceros gazelle antelope cheetah hippo leopard kangaroo crocodile deer rhino herbivore tortoise primate hyena camel scorpion macaque elephant mammoth alligator carnivore squirrel tiger newt chimpanzee monkey

exception: exemption limitation exclusion instance modification restriction recognition extension contrast addition refusal example clause indication definition error restraint reference objection consideration concession distinction variation occurrence anomaly offence jurisdiction implication analogy

pot: bowl pan jar container dish jug mug tin tub tray bag saucepan bottle basket bucket vase plate kettle teapot glass spoon soup box can cake tea packet pipe cup

VERBS

measure

determine assess calculate decrease monitor increase evaluate reduce detect estimate indicate analyse exceed vary test observe define record reflect affect obtain generate predict enhance alter examine quantify relate adjust

boil

simmer heat cook fry bubble cool stir warm steam sizzle bake flavour spill soak roast taste pour dry wash chop melt freeze scald consume burn mix ferment scorch soften

ADJECTIVES

hypnotic

haunting piercing expressionless dreamy monotonous seductive meditative emotive comforting expressive mournful healing indistinct unforgettable unreadable harmonic prophetic steely sensuous soothing malevolent irresistible restful insidious expectant demonic incessant inhuman spooky

pink

purple yellow red blue white pale brown green grey coloured bright scarlet orange cream black crimson thick soft dark striped thin golden faded matching embroidered silver warm mauve damp

Nearest neighbours

crane winch swan heron

winch crane heron tern

heron mast crane gull

tractor rigging gull swan

truck pump tern crane

swan tractor curlew flamingo

no clustering (tho’ could be done) no hierarchy (tho’ could be done) rhythm all on the web: http://wasps.itri.bton.ac.

uk– registration required

The web

an enormous linguist’s playground– Computational Linguistics Special Issue,

Kilgarriff and Grefenstette (eds) 29 (3)• (coming soon)

Google sets

http://labs.google.com/sets Input: zebra giraffe buffalo

Google sets

http://labs.google.com/sets Input: zebra giraffe buffalo kudu hyena impala leopard hippo

waterbuck elephant cheetah eland

Google sets

http://labs.google.com/sets Input: harbin beijing nanking

Google sets

http://labs.google.com/sets Input: harbin beijing nanking Output: shanghai chengdu guangzhou

hangzhou changchun zhejiang kunming dalian jinan fuzhou

Tree structure Roget

– all human knowledge as tree structure

– 1000 top categories• subdivisions

– like this» etc» etc

Directories and thesauruses

Yahoo, http://www.yahoo.com Open directory project, http://dmoz.org

– all human activity as tree structure

plus corpus at every node– gather corpus, identify domain vocabulary

• Gonzalo and colleagues, Madrid, CL Special Issue

• Agirre and colleagues, ‘topic signatures’

Words and word senses

automatic thesauruses– words

Words and word senses

automatic thesauruses– words

manual thesauruses– simple hierarchy is appealing– homonyms

Words and word senses

automatic thesauruses– words

manual thesauruses– simple hierarchy is appealing– homonyms– “aha! objects must be word senses”

Problems

Theoretical Practical

Theoretical

Wittgenstein

Don’t ask for the meaning, ask for the use

Practical

Problems

Practical– a thesaurus is a tool– if the tool organises words senses you must do

WSD before you can use it– WSD: state of the art, optimal conditions: 80%

.

Problems

Practical– a thesaurus is a tool– if the tool organises words senses you must do

WSD before you can use it– WSD: state of the art, optimal conditions: 80%

“To use this tool, first replace one fifth of your input with junk”

Avoid word senses

Avoid word senses

This word has three meanings/senses

Avoid word senses

This word has three meanings/senses This word has three kinds of use

– well founded– empirical– we can study it

sorry, roget

sorry, AI

sorry, AI AI model for NLP:

– NLP turns text into meanings– AI reasons over meanings– word meanings are concepts in an ontology– a Roget-like thesaurus is (to a good

approximation) an ontology– Guarino: “cleansing” WordNet

If a thesaurus groups words in their various uses (not meanings)– not the sort of thing AI can reason over

sorry, AI

“linguistics expressions prompt for meanings rather than express meanings”– Fauconnier and Turner 2003

It would be nice if … But …

Evaluation

manual thesauruses– not done

automatic thesauruses: attempts– pseudo-disambiguation (Lee 1999)– with ref to manual ones (Lin 1998)

Task-based evaluation

Task-based evaluation

Parsing– PP-attachment– conjunction scope

Bridging anaphors Text cohesion Word sense disambiguation (WSD) Speech understanding Spelling correction

What is performance at the task– with no thesaurus– with Roget– with WordNet– with WASPS

Plans

set up evaluation tasks theseval web-based thesaurus

– Open Directory Project hierarchies campaign

Cyborgs

Robots: will they take over? Rod Brooks’s answer:

– Wrong question: greatest advances are in what the human+computer ensemble can do

Cyborgs

A creature that is partly human and partly machine – Macmillan English Dictionary

Cyborgs and the Information Society

The thedsaurus-making agent is part human (for precision), part computer (for recall).

Summary: Thesauruses for NLP

Definition Uses for NLP WASPS thesaurus web thesauruses Argument: words not word senses Evaluation proposals Cyborgs

Thesaurus-makers of the future?