lecture 9: part of speech - university of virginia school...

Lecture 9: Part of Speech

Kai-Wei ChangCS @ University of Virginia

kw@kwchang.net

Couse webpage: http://kwchang.net/teaching/NLP16

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This lecture

vParts of speech (POS) vPOS Tagsets

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Parts of Speech

vTraditional parts of speechv~ 8 of them

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POS examples

vN noun chair, bandwidth, pacingvV verb study, debate, munchvADJ adjective purple, tall, ridiculousvADV adverb unfortunately, slowlyvP preposition of, by, tovPRO pronoun I, me, minevDET determiner the, a, that, those

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Parts of Speech

vA.k.a. parts-of-speech, lexical categories, word classes, morphological classes, lexical tags...

v Lots of debate within linguistics about the number, nature, and universality of these

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POS Tagging

vThe process of assigning a part-of-speech to each word in a collection (sentence).

WORD tag

the DETkoala Nput Vthe DETkeys Non Pthe DETtable N

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Why is POS Tagging Useful?

vFirst step of a vast number of practical tasksvParsing

v Need to know if a word is an N or V before you can parse

v Information extractionv Finding names, relations, etc.

vSpeech synthesis/recognitionv OBject obJECTv OVERflow overFLOWv DIScount disCOUNTv CONtent conTENT

vMachine Translation

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Open and Closed Classes

v Closed class: a small fixed membership v Prepositions: of, in, by, …v Pronouns: I, you, she, mine, his, them, …v Usually function words (short common words which

play a role in grammar)

v Open class: new ones can be createdv English has 4: Nouns, Verbs, Adjectives, Adverbsv Many languages have these 4, but not all!

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Open Class Words

v Nounsv Proper nouns (Boulder, Granby, Eli Manning)v Common nouns (the rest). v Count nouns and mass nouns

v Count: have plurals, get counted: goat/goats, one goat, two goats

v Mass: don’t get counted (snow, salt, communism) (*two snows)

v Verbsv In English, have morphological affixes (eat/eats/eaten)

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Closed Class Words

Examples:vprepositions: on, under, over, …vparticles: up, down, on, off, …vdeterminers: a, an, the, …vpronouns: she, who, I, ..vconjunctions: and, but, or, …vauxiliary verbs: can, may should, …vnumerals: one, two, three, third, …

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Prepositions from CELEX

CELEX:onlinedictionaryFrequencycountsarefromCOBUILD16-billion-wordcorpus

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English Particles

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Conjunctions

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Choosing a Tagset

v Could pick very coarse tagsetsv N, V, Adj, Adv, Other

v More commonly used set is finer grainedv E.g., “Penn TreeBank tagset”, 45 tags: PRP$, WRB,

WP$, VBGv Brown cropus, 87 tags.

v Prague Dependency Treebank (Czech)v 4452 tagsv AAFP3----3N----: (nejnezajímavějším)

Adj Regular Feminine Plural….Superlative [Hajic 2006, VMC tutorial]

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Penn TreeBank POS Tagset

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Using the Penn Tagset

vThe/DT grand/JJ jury/NN commmented/VBD on/IN a/DT number/NN of/IN other/JJ topics/NNS ./.

Universal Tag set

v ~ 12 different tagsvNOUN, VERB, ADJ, ADV, PRON, DET, ADP,

NUM, CONJ, PRT, “.”, X

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POS Tagging v.s. Word clustering

vWords often have more than one POS: backvThe back door = JJvOn my back = NNvWin the voters back = RBvPromised to back the bill = VB

These examples from Dekang Lin

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How Hard is POS Tagging?

POS tag sequences

vSome tag sequences more likely occur than others

vPOS Ngram viewhttps://books.google.com/ngrams/graph?content=_ADJ_+_NOUN_%2C_ADV_+_NOUN_%2C+_ADV_+_VERB_

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ExistingmethodsoftenmodelPOStaggingasasequencetagging problem

Evaluation

vHow many words in the unseen test data can be tagged correctly?

vUsually evaluated on Penn TreebankvState of the art ~97% vTrivial baseline (most likely tag) ~94%vHuman performance ~97%

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