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What computers can and cannot do for lexicography or Us precision, them recall

What computers can and cannot do for lexicography or Us precision, them recall

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What computers can and cannot do for lexicography or Us precision, them recall

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  1. What computers can and cannot do for lexicographyorUs precision, them recall Adam Kilgarriff Lexicography Masterclass Ltd and University of Brighton, UK

  2. Outline • Precision and recall • History of corpus lexicography • Natural Language Processing • Cyborgs Adam Kilgarriff: Us precision them recall

  3. Find me all the fat cats • a request for information Adam Kilgarriff: Us precision them recall

  4. High recall • Lots of responses • Maybe not all good Adam Kilgarriff: Us precision them recall

  5. High precision • Fewer hits • Higher confidence Adam Kilgarriff: Us precision them recall

  6. Us precision, them recall Adam Kilgarriff: Us precision them recall

  7. Us precision, them recall • True in many areas • web searching, google • finding an image to illustrate a talk • Nowhere more so than lexicography Adam Kilgarriff: Us precision them recall

  8. Lexicography: finding facts about words • collocations • grammatical patterns • idioms • synonyms • antonyms • meanings • translations Adam Kilgarriff: Us precision them recall

  9. Outline • Precision and recall • History of corpus lexicography • Natural Language Processing • Cyborgs Adam Kilgarriff: Us precision them recall

  10. Four ages of corpus lexicography Adam Kilgarriff: Us precision them recall

  11. Age 1: • Pre • computer • Oxford English • Dictionary: • 5 million • index cards Adam Kilgarriff: Us precision them recall

  12. Age 2: KWIC Concordances • From 1980 • Computerised • COBUILD project was innovator • asian-kwic.html • the coloured-pens method Adam Kilgarriff: Us precision them recall

  13. 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 Adam Kilgarriff: Us precision them recall

  14. 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 Adam Kilgarriff: Us precision them recall

  15. Collocation listing For right collocates of save (>5 hits) Adam Kilgarriff: Us precision them recall

  16. Collocation statistics • Which words? • next word • last word • window, +1 to +5; window, -5 to -1 • How sorted? • most common collocates --but for most nouns it's the • most salient collocates --how to measure salience? Adam Kilgarriff: Us precision them recall

  17. Mutual Information • Church and Hanks 1989 • How much more often does a word pair occur, than one might expect by chance • “Chance” of x and y occurring together: p(x) * p(y) • Probabilitiesapproximated by frequencies p(x) =(approx) f(x)/N Adam Kilgarriff: Us precision them recall

  18. Mutual Information * numbers are log-proportional to MI Adam Kilgarriff: Us precision them recall

  19. Problem • mathematical salience = lexicographic salience? • no! higher-frequency items are lexicographically more salient • Solution multiply MI by raw frequency Adam Kilgarriff: Us precision them recall

  20. Mutual Information Adam Kilgarriff: Us precision them recall

  21. Collocation listing For right collocates of save (>5 hits) Adam Kilgarriff: Us precision them recall

  22. Age-3 collocation statistics: limitations Lists contain • junk • unsorted for type --MI lists mix adverbs, subjects, objects, prepositions What we really want: • noise-free lists • one list for each grammatical relation Adam Kilgarriff: Us precision them recall

  23. 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 Adam Kilgarriff: Us precision them recall

  24. Can we do it? • high-accuracy parsing is hard • lots of NLP work, many parsing frameworks exist • if any parser can handle large corpus, it's probably good enough--- sorting, statistics, make us error-tolerant Adam Kilgarriff: Us precision them recall

  25. Can we do it? • high-accuracy parsing is hard • lots of NLP work, many parsing frameworks exist • if any parser can handle large corpus, it's probably good enough--- sorting, statistics, make us error-tolerant • Poor man’s parsing: • object (of active verb) = last noun in any sequence of nouns, adjectives, determiners, numbers and adverbs following the verb Adam Kilgarriff: Us precision them recall

  26. The word sketch • coffee_n.html Adam Kilgarriff: Us precision them recall

  27. Macmillan Dictionary of English for Advanced Leaners, 2002: editor: Rundell. Work done 1999. • Word sketches produced for 6000 most common nouns, verbs, adjectives of English • using British National Corpus (100 M words, already POS-tagged) • lemmatized using John Carroll's lemmatizer • parsed using regular expressions over POS-tags • HTML files with hyperlinked corpus examples • lexicographers used them extensively, used instead of going direct to corpus • positive feedback Adam Kilgarriff: Us precision them recall

  28. Outline • Precision and recall • History of corpus lexicography • Natural Language Processing • Cyborgs Adam Kilgarriff: Us precision them recall

  29. Natural Language Processing • The academic discipline which provides the tools • Also known as Computational Linguistics, Human Language Technology (HLT), Language Engineering • Good at evaluation of its tools • Good news for lexicography: • identify the best tools, apply them to our corpora Adam Kilgarriff: Us precision them recall

  30. An Anglophone Apology • Technology, tools, resources most often available for English • This talk centres on English • Other languages often present new problems • Finding word delimiters for Chinese is hard • Finding bunsetsu for Japanese is hard • Fewer resources available, less work done • Recommendation: • find the local experts for your language Adam Kilgarriff: Us precision them recall

  31. Recap: Lexicography: finding facts about words • collocations • grammatical patterns • idioms • synonyms • antonyms • meanings • translations Adam Kilgarriff: Us precision them recall

  32. Recap: Lexicography: finding facts about words • collocations - sketches • grammatical patterns - sketches • idioms • synonyms • antonyms • meanings • translations Adam Kilgarriff: Us precision them recall

  33. Idioms • Extreme case of collocation/multi word expressions • Sequence of workshops on collocations, MWE • Technical terms (of great interest to technologists, technical): TERMIGHT Adam Kilgarriff: Us precision them recall

  34. Antonyms • Essential semantic relation Adam Kilgarriff: Us precision them recall

  35. Antonyms • Essential semantic relation but • Justeson and Katz 1995: distributional evidence for typical antonym pairs • rich men and poor men • the big ones and the small ones • black and white issues • Perhaps antonyms are ‘really’ distributional Adam Kilgarriff: Us precision them recall

  36. Thesauruses • Also near-synonyms • are there any true synonyms? • Distributional: which words share same distributions • if corpus contains object(drink, wine), object(drink, beer) • 1 pt similarity between wine and beer • gather all points; find nearest neighbours • Sparck Jones, Lin, Grefenstette Adam Kilgarriff: Us precision them recall

  37. Nearest neighbours Adam Kilgarriff: Us precision them recall

  38. Translation • Parallel corpora • Texts and their translations or • Comparable corpora • Matched for source and target (genre and subject matter), not translations • Which L1 words occur in equivalent L1 settings to L2 words in L2 settings? • They are candidate translation pairs • Very hard problem • Lots of high quality research Adam Kilgarriff: Us precision them recall

  39. The WASPbench • with David Tugwell, supported by UK EPSRC, grant M54971 A lexicographer's workbench • runtime creation of word sketches • integration with Word Sense Disambiguation technology • output is "disambiguating dictionary" - analysis of word's meaning into senses, plus computer program for disambiguating contextualised instances of the word • First release now available. http://wasps.itri.brighton.ac.uk/ • Sketches at http://www.itri.brighton.ac.uk/~Adam.Kilgarriff/wordsketches.html Adam Kilgarriff: Us precision them recall

  40. 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 • First release early 2004 Adam Kilgarriff: Us precision them recall

  41. Outline • Precision and recall • History of corpus lexicography • Natural Language Processing • Cyborgs Adam Kilgarriff: Us precision them recall

  42. Cyborgs • Robots: will they take over? • Rod Brooks’s answer: • Wrong question: greatest advances are in what the human+computer ensemble can do Adam Kilgarriff: Us precision them recall

  43. Cyborgs • A creature that is partly human and partly machine • Macmillan English Dictionary Adam Kilgarriff: Us precision them recall

  44. Adam Kilgarriff: Us precision them recall

  45. Adam Kilgarriff: Us precision them recall

  46. Adam Kilgarriff: Us precision them recall

  47. Adam Kilgarriff: Us precision them recall

  48. Cyborgs and the Information Society The dictionary-making agent is part human (for precision), part computer (for recall). Adam Kilgarriff: Us precision them recall

  49. Treat your computer with respect. You and it can do great things together. Adam Kilgarriff: Us precision them recall

  50. Lexicographers of the future? Adam Kilgarriff: Us precision them recall