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8. Word Classes and Part-of-Speech Tagging
2007년 5월 26일
인공지능 연구실 이경택
Text: Speech and Language Processing
Page.287 ~ 303
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.
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
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
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
Open Classes




Noun
Verb
Adjective
Adverb
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).
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)
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
Adjectives
 Terms that describe properties or qualities
 Concept of color, age, value, ……
 There are languages without adjectives. (ex. Chinese)
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
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
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
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).
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
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.
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.
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.
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
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)
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))
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.
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
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)
ENGTWOL – Process1
 Process
 First stage: Each word is run through the two-level lexicon transducer and the
all possible POS are returned.
- Ex.
- Pavlov
- had
- shown
- that
- salivation
PAVLOV N NOM SG PROPER
HAVE V PAST VFIN SVO
HAVE PCP2 SVO
SHOW PCP2 SVOO SVO SV
ADV
PRON DEM SG
DET CENTRAL DEM SG
CS
N NOM SG
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