8. Word Classes and Part-of-Speech Tagging

1. 8. Word Classes and Part-of-Speech Tagging 2007년 5월 26일 인공지능 연구실 이경택 Text: Speech and Language Processing Page.287 ~ 303

2. Origin of POS • Techne: a grammatical sketch of Greek which is written by Dionysius Thrax of Alexandria (c. 100 B.C.) or someone else. • Eight parts-of-speech: noun, verb, pronoun, preposition, adverb, conjunction, participle, article • The basis for practically all subsequent part-of-speech descriptions of Greek, Latin and most European language for the next 2000 years.

3. Recent Lists of POS • Recent POS list have much larger than before • Penn Treeback (Marcus et al., 1993): 45 • Brown corpus (Francis, 1979; Francis and Kučera, 1982): 87 • C7 tagset (Garside et al., 1997): 146 • Synonym of POS • word classes • morphological classes • lexical tags

4. POS can be used in • Recognize or Produce pronunciation of words • CONtent (noun), conTENT (adjective) • Object (noun), obJECT (adjective) • …… • In information retrieval • Stemming • Select out nouns or other important words • ASR language model like class-based N-grams • Partial parsing

5. 8.1 (Mostly) English Word Classes • Closed class types: have relatively fixed membership • Ex. Prepositions: new prepositions are rarely coined. • Generally function words (ex. of, it, and, or, ……) • Very short • Occur frequently • Play an important role in grammar • Open class types: have relatively updatable membership • Ex. Noun and verb: new words continually coined or borrowed from other language. • Four major open classes (but not all of human language have all of these) • Nouns • Verbs • Adjectives • Adverbs

6. Open Classes • Noun • Verb • Adjective • Adverb

7. Definition of Noun • Functional definition is not good • The name given to the lexical class in which the words for most people, places, or things occur • Bandwidth? Relationship? Pacing? • Semantic definition of noun • Thing like its ability to occur with determiners (a goat, its bandwidth, Plato’s Republic), to take (IBM’s annual revenue), and for most but not all nouns, to occur in the plural form (goats, abaci).

8. Grouping Noun • Uniqueness • Proper nouns: Regina, Colorado, IBM, …… • Common nouns: book, stair, apple, …… • Countable • Count nouns • Can occur in both the singular and plural: goat(s), relationship(s), …… • Can be counted: (one, two, ……) goat(s) • Mass nouns • Cannot be counted: two snows (x), two communisms (x) • Can appear without articles where singular count nouns cannot: Snow is white (o), Goat is white (x)

9. Verbs • Verbs have a number of morphological forms • Non-3rd-person-sg: eat • 3rd-person-sg: eats • Progressive: eating • Past participle: eaten • Auxiliaries: subclass of English verbs

10. Adjectives • Terms that describe properties or qualities • Concept of color, age, value, …… • There are languages without adjectives. (ex. Chinese)

11. Adverbs • Directional adverbs, locative adverbs: specify the direction or location of some action • Ex. home, here, downhill • Degree adverbs: specify the extent of some action, process, or property • Ex. extremely, very, somewhat • Manner adverbs, temporal adverbs: describe the time that some action or event took place • Ex. yesterday, Monday • Some adverbs (ex. Monday) are tagged in some tagging schemes as nouns

12. Closed Classes • Prepositions: on, under, over, near, by, at, from, to, with • Determiners: a, an, the • Pronouns: she, who, I, others • Conjunctions: and, but, or, as, if, when • Auxiliary verbs: can, may, should, are • Particles: up, down, on, off, in, out, at, by • Numerals: one, two, three, first, second, third

13. Prepositions • Occur before noun phrases • Often indicating spatial or temporal relations • Literal (ex. on it, before then, by the house) • Metaphorical (on time, with gusto, beside herself) • Often indicate other relations as well • Ex. Hamlet was written by Shakespeare, and [from Shakespeare] “And I did laugh sans intermission an hour by his dial” Figure 8.1 Prepositions (and particles) of English from the CELEX on-line dictionary. Frequently counts are from the COBUILD 16 million word corpus

14. Particle • Often combines with a verb to form a larger unit called a phrasal verb • Come in: adjective • Come with: preposition • Come on: particle Figure 8.2 English single-word particles from Quirk et al. (1985).

15. Determiners (articles) • a, an: mark a noun phrase as indefinite • the: mark it as definite • this?, that? • COBUILD statistics out of 16 million words • the: 1,071,676 • a: 413,887 • an: 59,359

16. Conjunctions • Used to join two phrases, clauses, sentences • Coordinating conjunction: equal status • and, or, but • Subordinating conjunction: embedded status • that (ex. I thought that you might like some milk) • complementizers: Subordinating conjunctions like that which link a verb to its argument in this way (more: Chapter 9, 11) Figure 8.3 Coordinating and subordinating conjunctions of English from the CELEX on-line dictionary. Frequency counts are from the COBUILD 16 million word corpus.

17. Pronouns • A kind of shorthand for referring to some noun phrase or entity or event. • Personal pronouns: you, she, I, it, me, …… • Possessive pronouns: my, your, his, her, its, one’s, our, their, …… • Wh-pronouns: what, who, whom, whoever Figure 8.4 Pronouns of English from the CELEX on=line dictionary. Frequency counts are from the COBUILD 16 million word corpus.

18. Auxiliary Verbs • Words that mark certain semantic features of a main verb = modal verb • be: copula verb • do • have: perfect tenses • can: ability, possibility • may: permission, possibility • …… Figure 8.5 English modal verbs from the CELEX on-line dictionary. Frequency counts are from the COBUILD 16 million word corpus.

19. Other Closed Classes • Interjections • on, ah, hey, man, alas, …… • negatives • no, not, …… • politeness markers • please, thank you, …… • greetings • hello, goodbye, …… • existential there • There are two on the table

20. 8.2 Tagsets for English • There are various tagsets for English. • Brown corpus (Francis, 1979; Francis and Kučera, 1982): 87 tags • Penn Treebank (Marcus et al., 1993): 45 tags • British National Corpus (Garside st al., 1997): 61 tags (C5 tagset) • C7 tagset: 164 tags • Which tagset to use for a particular application depends on how much information the application needs Figure 8.6 Penn Treebank part-of-speech tags (including punctuation)

21. 8.3 Part-of-Speech Tagging • Definition: Process of assigning a POS or other lexical class marker to each word in a corpus. • Input: a string of words, tagset (ex. Book that flight, Penn Treebank tagset) • Output: a single best tag for each word (ex. Book/VB that/DT flight/NN ./.) • Problem: resolve ambiguity → disambiguation • Ex. book (Hand me that book, Book that flight) Figure 8.7 The number of word types in Brown corpus by degree of ambiguity (after DeRose(1988))

22. Taggers • Rule-based taggers • Generally involve a large database of hand-written disambiguation rule • Ex. ENGTWOL (based on the Constraint Grammar architecture of Karlsson et al. (1995)) • Stochastic taggers • Generally resolve tagging ambiguities by using a training corpus to compute the probability of a given word having a given tag in a given context. • Ex. HMM tagger(=Maximum Likelihood Tagger = Markov model tagger, based on the Hidden Markov Model) • Transformation-based tagger, Brill tagger (after Brill(1995)) • Shares features of rule-based tagger and stochastic tagger • The rules are automatically induced from a previously tagged training corpus.

23. 8.4 Rule-Based Part-of-Speech Tagging • Earliest algorithm • Based on two-stage architecture • First stage: assign each word a list of potential POS using dictionary • Second stage: winnow down the lists using hand-written disambiguation rule

24. ENGTWOL (Voutilainen, 1995) • lexicon • Based on two-level morphology • Using 56,000 entries for English word stems (Heikkilä, 1995) • Counting a word with multiple POS as separate entries Figure 8.8 Sample lexical entries from the ENGTWOL lexicon described in Voutilainen (1995) and Heikkilä (1995)

25. ENGTWOL – Process1 • Process • First stage: Each word is run through the two-level lexicon transducer and the all possible POS are returned. • Ex. • Pavlov PAVLOV N NOM SG PROPER • had HAVE V PAST VFIN SVO • HAVE PCP2 SVO • shown SHOW PCP2 SVOO SVO SV • that ADV • PRON DEM SG • DET CENTRAL DEM SG • CS • salivation N NOM SG

26. ENGTWOL – Process2 • Second stage • Eliminate tags that are inconsistent with the context using a set of about 1,100 constraints in negative way • Ex. • Adverbial-that rule • Given input: “that” • if • (+1 A/ADV/QUANT); /* if next word is adj, adverb, or quantifier */ • (+2 SENT-LIM); /* and following which is a sentence boundary, */ • (NOT -1 SVOC/A); /* and the previous word is not a verb like */ • /* ‘consider’ which allows adjs as object complements */ • then eliminate non-ADV tags • else eliminate ADV tag • Also uses • Probabilistic constraints • Other syntactic information