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A cascade of corpora:The Cambridge Learner Corpus,

English Profile, the Sketch Engine, HOO, DANTE and the Kelly Project

Adam KilgarriffLexical Computing Ltd

http://www.sketchengine.co.uk

English Profile

• From 2006• Cambridge Univ, Univ Press, ESOL (+ others)• Goal

– for each CEFR level, find characteristic lexis and grammar

• CEFR: Common European Framework of Reference– A1, A2: Beginner– B1, B2: Intermediate– C1, C2: Advanced

– Main resource: CLCNTNU Nov 2011 KIlgarriff 2

Cambridge Learner Corpus (CLC)

• Since 1993 • Leading resource• CUP and Cambridge Assessment

– For better dictionaries, ELT courses, tests– Material: all from exams (levels A1-C2)

• 45m words; 22m error-tagged• 200,000 scripts, 138 L1s, 203 nationalities

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Sketch Engine

• Leading corpus tool• Word sketches

– One-page summaries of a word’s grammatical and collocational behaviour

• In use at OUP, CUP, Collins, Macmillan, INL …• 55 languages

– 175 corpora– Since May including CHILDES: demo– Since last year including CLC


http://sketchengine.co.uk/


Macmillan English DictionaryFor Advanced Learners

Ed: Rundell, 2002

Error-coded corpus

• Challenge– Intuitive to search for x

• anywhere• only where it is part of an error• only where it is part of a correction

where x can be a word, phrase, grammar pattern …

Requirement for CLC in Sketch Engine


Error-coded corpora in SkE

• demo


http://cup.sketchengine.co.uk/

HOO / HOO+

• Helping Our Own• HOO: English-NNS NLP researchers

– Developer = user: motivation– Shared task/competitive evaluation

• Organisers define task and prepare ‘gold standard’• Teams participate by running their software over test

data• Six teams (incl Tübingen), workshop end Sept


HOO+ (2012)

• Probably– English: learner data from CLC– Other languages? – Tasks

• Essay scoring • Determiner, preposition errors• ?• http://www.clt.mq.edu.au/research/projects/hoo/


DANTE

Highlights of English lexicography


DANTE


DANTE


DANTE


DANTE

http://webdante.com


http://webdante.com/

The KELLY Project

• EU Lifelong Learning Project• Word cards

– 9 languages• Arabic Chinese English Greek Italian Norwegian Polish

Russian Swedish– All 36 pairs– Words the learner should know (at A1 … C2)

• Partners• Stockholm Univ, Gotheburg Univ, Adam Mickiewicz Univ,

ILSP Athens, CNR Pisa, Oslo Univ, Leeds Univ, Keewords A/S, Lexical Computing Ltd


Interesting question

• How close to purely corpus-based can a pedagogic list be?


Method

• Take a general corpus• Count• Review, add, delete using other lists and corpora• Translate (72 directed-lg-pairs)• Words not in source list which occur in

translations:– Review source list

• http://kelly.sketchengine.co.uk


http://kelly.sketchengine.co.uk/

• Symmatrical pairs: <x,y> and <y,x>• Cliques:

– For x, y, z, … all pairs are symmetrical– 9-language cliques (English members)

• hospital library music sun theory



Web corpora

• Replaceable or replacable?– http://googlefight.com – http://looglefight.com

http://googlefight.com/

http://looglefight.com/


• The web is– Very very large– Most languages– Most language types– Up-to-date– Free– Instant access


Web corpus types

• Large, general corpora• Small, specialised corpora

– Specially for translators


Basic steps• Gather pages

– CSE hits– Select and gather whole sites– General crawl

• Filter• De-duplicate• Linguistic processing• Load into corpus tool


WaC family corpora• 100m – 2b word corpora• 2-month project each• All major world languages available in Sketch Engine

– Currently 42 languages– Growing monthly

• Pioneers: Marco Baroni, Serge Sharoff• Corpus Factory

• Seeds: – mid-frequency words from ‘core vocab’ lists and corpora

• Google on seed words, then crawl


How good are they?• How to assess?

– Hard question, open research topic• Good coverage

– Newspapers: news, politics bias– Web corpora: also cover personal, kitchen

vocab• Web corpus / BNC / journalism corpus

– First two are close


Evaluating word sketches

• 11 years – 1999-2011

• Feedback– Good but anecdotal

• Formal evaluation• Method also lets us evaluate corpora

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Goal

• Collocations dictionary– Model: Oxford Collocations Dictionary– Publication-quality

• Ask a lexicographer– For 42 headwords

• For 20 best collocates per headwords– “should we include this collocation in a published

dictionary?”

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Sample of headwords• Nouns verbs adjectives, random• High (Top 3000)• N space solution opinion mass corporation leader• V serve incorporate mix desire• Adj high detailed open academic• Mid (3000- 9999)• N cattle repayment fundraising elder biologist sanitation• V grieve classify ascertain implant• Adj adjacent eldest prolific ill• Low (10,000- 30,000)• N predicament adulterer bake bombshell candy shellfish• V slap outgrow plow traipse• Adj neoclassical votive adulterous expandable

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Precision and recall

• a request for information– Find me all the fat cats


High recall

• Lots of responses• Maybe not all good


High precision

• Fewer hits• Higher confidence

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Precision and recall We test precision Recall is harder

How do we find all the collocations that the system should have found?

Current work• 200 collocates per headword

• Selected from

• All the corpora we have

• Various parameter settings

• Plus just-in-time evaluation for 'new' collocates

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Four languages, three families

• Dutch– ANW, 102m-word lexicographic corpus

• English– UKWaC, 1.5b web corpus

• Japanese– JpWaC, 400m web corpus

• Slovene – FidaPlus, 620m lexicographic corpus

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User evaluation

• Evaluate whole system– Will it help with my task

• Eg preparing a collocations dictionary

• Contrast: developer evaluation– Can I make the system better?

• Evaluate each module separately• Current work

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Components

• Corpus• NLP tools

– Segmenter, lemmatiser, POS-tagger

• Sketch grammar• Statistics

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Practicalities• Interface

– Good, Good-but• Merge to good

– Maybe, Maybe-specialised, Bad• Merge to bad

• For each language– Two/three linguists/lexicographers– If they disagree

• Don't use for computing performance

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Results

• Dutch 66%• English 71%• Japanese 87%• Slovene 71%

NTNU Nov 2011


Two thirds of a collocations dictionary can be gathered automatically

Thank you

http://www.sketchengine.co.uk


http://www.sketchengine.co.uk/



Lexicography: finding facts about words

• collocations• grammatical patterns• idioms• synonyms• meanings• translations


Four ages of corpus lexicography


Age 1:Precomputer

Oxford English Dictionary:• 5 million index cards


Age 2: KWIC Concordances

• From 1980• Computerised• Overhauled lexicography


Age 2: limitations

as corpora get bigger:too much data

• 50 lines for a word: :read all • 500 lines: could read all, takes a long time, slow • 5000 lines: no


Age 3: Collocation statistics

• Problem:too much data - how to summarise?

• Solution:list of words occurring in neighbourhood of headword, with frequencies

• Sorted by salience


Age-3 collocation statistics: limitations

Lists contain• junk • unsorted for type – mixes together adverbs,

subjects, objects, prepositions

What we really want: • noise-free lists • one list for each grammatical relation


Age 4: The word sketch• Large well-balanced corpus• Parse to find

– subjects, objects, heads, modifiers etc

• One list for each grammatical relation• Statistics to sort each list, as before


Working practice

• Lexicographers mainly used sketches not concordances – missed less, more consistent– Faster


Euralex 2002


Euralex 2002

• Can I have them for my language please


The Sketch Engine

• Input: – any corpus, any language

• Lemmatised, part-of-speech tagged– specification of grammatical relations

• Word sketches integrated with• Corpus query system

– Supports complex searching, sorting etc• Credit: Pavel Rychly, Masaryk Univ


Customers• Dictionary publishers

– Oxford University Press– Cambridge University Press– Collins– National dictionary projects in

• Czech Republic, Estonia, Ireland, Netherlands, Slovakia, Slovenia

• Universities– Teaching and research– Languages, linguistics, language technology– UK, Germany, US, Greece, Taiwan, Japan, China, …

• Other– Language teaching, textbook writing– Information management, web search


• Demo– http://sketchengine.co.uk– Free trial

http://sketchengine.co.uk/


What is there on the web?• Web1T

– Present from google– All 1-, 2-, 3-, 4, 5-grams with f>40 in one trillion

(1012) words of English• 1,000,000,000,000

• Compare with BNC– Take top 50,000 items of each– 105 Web1T words not in BNC top50k– 50 words with highest Web1T:BNC ratio– 50 words with lowest ratio


Web-high (155 terms)

• 61 web and computing– config browser spyware url www forum

• 38 porn• 22 US English• 18 business/products common on web

– poker viagra lingerie ringtone dvd casino rental collectible tiffany

– NB: BNC is old• 4 legal

– trademarks pursuant accordance herein


Web-low

• Exclude British English, transcription/tokenisation anomalies

– herself stood seemed she looked yesterday sat considerable had council felt perhaps walked round her towards claimed knew obviously remained himself he him


Observations• Pronouns and past tense verbs

– Fiction

• Masc vs fem• Yesterday

– Probably daily newspapers

• Constancy of ratios:– He/him/himself– She/her/herself