The BNC Design Model Adam Kilgarriff, Sue Atkins, Michael Rundell The Lexicography MasterClass .
1 What computers can and cannot do for lexicography or Us precision, them recall Adam Kilgarriff...
-
Upload
dana-pentecost -
Category
Documents
-
view
215 -
download
0
Transcript of 1 What computers can and cannot do for lexicography or Us precision, them recall Adam Kilgarriff...
1
What computers can and cannot do for lexicography
or
Us precision, them recall
Adam Kilgarriff
Lexicography Masterclass Ltd
and
University of Brighton, UK
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 2
Outline Precision and recall History of corpus lexicography Natural Language Processing Cyborgs
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 3
Find me all the fat cats
a request for information
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 4
High recall
Lots of responses Maybe not all good
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 5
High precision
Fewer hits Higher confidence
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 6
Us precision, them recall
Recall Precision
Computers good bad
People bad good
27-29 Aug 2003 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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 8
Lexicography: finding facts about words
collocations grammatical patterns idioms synonyms antonyms meanings translations
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 9
Outline Precision and recall History of corpus lexicography Natural Language Processing Cyborgs
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 10
Four ages of corpus lexicography
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 11
Age 1:Precomputer
Oxford English Dictionary:• 5 million index cards
27-29 Aug 2003 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
27-29 Aug 2003 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
27-29 Aug 2003 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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 15
Collocation listingFor right collocates of save (>5 hits)
word fr(x+y) fr(y) word fr(x+y) fr(y)
forests 6 170 life 36 4875
$1.2 6 180 dollars 8 1668
lives 37 1697 costs 7 1719
enormous 6 301 thousands 6 1481
annually 7 447 face 9 2590
jobs 20 2001 estimated 6 2387
money 64 6776 your 7 3141
27-29 Aug 2003 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?
27-29 Aug 2003 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) Probabilities approximated by
frequencies
p(x) =(approx) f(x)/N
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 18
Mutual Information
X fr eat fr X fr eat X
MI* rank
it 1000 400,000
400 1/
1M
3
meat 1000 6000 120 20/
1M
2
sushi 1000 100 8 80/
1M
1
* numbers are log-proportional to MI
27-29 Aug 2003 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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 20
Mutual Information
X fr eat fr X fr eat X
MI rank
it 1000 400,000
400 1/M 3
meat 1000 6000 120 20/M 2
sushi 1000 100 8 80/M 1
MI x fr
new rank
400/M
3
2400/M
1
640/M
2
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 21
Collocation listingFor right collocates of save (>5 hits)
word fr(x+y) fr(y) word fr(x+y) fr(y)
forests 6 170 life 36 4875
$1.2 6 180 dollars 8 1668
lives 37 1697 costs 7 1719
enormous 6 301 thousands 6 1481
annually 7 447 face 9 2590
jobs 20 2001 estimated 6 2387
money 64 6776 your 7 3141
27-29 Aug 2003 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
27-29 Aug 2003 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
27-29 Aug 2003 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
27-29 Aug 2003 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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 26
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 27
The word sketch British National Corpus (BNC)
– 100 M words, already POS-tagged lemmatized using John Carroll's lemmatizer poor man’s parsing database of 70 million triples
<object, sip, coffee> <subject, arrive, coffee>
<and-or, tea, coffee> <modifier, coffee, instant>
coffee_n.html
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 28
Macmillan Dictionary of English for Advanced Leaners, 2002
Editor: Rundell.
Work done 1999. 6000 word sketches
– most common nouns, verbs, adjectives of English HTML files with hyperlinked corpus examples lexicographers used them extensively
– main use of corpus positive feedback
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 29
The WASPbench with David Tugwell, 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/
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 30
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 31
Outline Precision and recall History of corpus lexicography Natural Language Processing Cyborgs
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 32
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 33
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 34
Recap: Lexicography: finding facts about words
collocations grammatical patterns idioms synonyms antonyms meanings translations
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 35
Recap: Lexicography: finding facts about words
collocations - sketches grammatical patterns - sketches idioms synonyms antonyms meanings translations
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 36
Idioms Extreme case of collocation/multi word
expressions Sequence of workshops on
collocations, MWE Technical terms (of great interest to
technologists, technical): TERMIGHT
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 37
Antonyms Essential semantic relation
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 38
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 39
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 40
Nearest neighbours In WASPbench Will be generated in Sketch Engine
NOUNS
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 41
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 42
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 43
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 44
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
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 45
Outline Precision and recall History of corpus lexicography Natural Language Processing Cyborgs
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 46
Cyborgs Robots: will they take over? Rod Brooks’s answer:
– Wrong question: greatest advances are in what the human+computer ensemble can do
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 47
Cyborgs A creature that is partly human and
partly machine – Macmillan English Dictionary
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 48
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 49
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 50
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 51
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 52
Cyborgs and the Information Society
The dictionary-making agent is part human (for precision), part computer (for recall).
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 53
Treat your computer with respect. You and it can do
great things together.
27-29 Aug 2003 Adam Kilgarriff: Us precision them recall 54
Lexicographers of the future?