LINGUISTICA GENERALE E COMPUTAZIONALE DISAMBIGUAZIONE DELLE PARTI DEL DISCORSO.
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LINGUISTICA GENERALE E COMPUTAZIONALE DISAMBIGUAZIONE DELLE PARTI DEL DISCORSO
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Transcript of LINGUISTICA GENERALE E COMPUTAZIONALE DISAMBIGUAZIONE DELLE PARTI DEL DISCORSO.
LINGUISTICA GENERALE E COMPUTAZIONALE
DISAMBIGUAZIONE DELLE PARTI DEL DISCORSO
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POS tagging: the problem
• People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN
• Problem: assign a tag to race• Requires: tagged corpus
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Ambiguity in POS tagging
The ATman NN VBstill NN VB RBsaw NN VBDher PPO PP$
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How hard is POS tagging?
Number of tags 1 2 3 4 5 6 7
Number of words types
35340 3760 264 61 12 2 1
In the Brown corpus,- 11.5% of word types ambiguous- 40% of word TOKENS
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Frequency + Context
• Both the Brill tagger and HMM-based taggers achieve good results by combining– FREQUENCY
• I poured FLOUR/NN into the bowl.• Peter should FLOUR/VB the baking tray
– Information about CONTEXT • I saw the new/JJ PLAY/NN in the theater.• The boy will/MD PLAY/VBP in the garden.
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The importance of context
• Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN
• People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN
TAGGED CORPORA
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Choosing a tagset
• The choice of tagset greatly affects the difficulty of the problem
• Need to strike a balance between– Getting better information about context (best:
introduce more distinctions)– Make it possible for classifiers to do their job
(need to minimize distinctions)
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Some of the best-known Tagsets
• Brown corpus: 87 tags• Penn Treebank: 45 tags• Lancaster UCREL C5 (used to tag the BNC): 61
tags• Lancaster C7: 145 tags
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Important Penn Treebank tags
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Verb inflection tags
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The entire Penn Treebank tagset
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UCREL C5
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Tagsets per l’italiano
Si-TAL (Pisa, Venezia, IRST, ....)
PAROLE
TEXTPRO (dopo)
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Il tagset di SI-TAL
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POS tags in the Brown corpus
Television/NN has/HVZ yet/RB to/TO work/VB out/RP a/AT living/RBG arrangement/NN with/IN jazz/NN ,/, which/VDT comes/VBZ to/IN the/AT medium/NN more/QL as/CS an/AT uneasy/JJ guest/NN than/CS as/CS a/AT relaxed/VBN member/NN of/IN the/AT family/NN ./.
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SGML-based POS in the BNC
<div1 complete=y org=seq> <head> <s n=00040> <w NN2>TROUSERS <w VVB>SUIT </head> <caption> <s n=00041> <w EX0>There <w VBZ>is <w PNI>nothing <w AJ0>masculine <w PRP>about <w DT0>these <w AJ0>new <w NN1>trouser <w NN2-VVZ>suits <w PRP>in <w NN1>summer<w POS>'s <w AJ0>soft <w NN2>pastels<c PUN>. <s n=00042> <w NP0>Smart <w CJC>and <w AJ0>acceptable <w PRP>for <w NN1>city <w NN1-VVB>wear <w CJC>but <w AJ0>soft <w AV0>enough <w PRP>for <w AJ0>relaxed <w NN2>days </caption>
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Quick test
DoCoMo and Sony are to develop a chip that would let people pay for goods through their mobiles.
POS TAGGED CORPORA IN NLTK
>>> tagged_token = nltk.tag.str2tuple('fly/NN') >>> tagged_token('fly', 'NN') >>> tagged_token[0] 'fly' >>> tagged_token[1] 'NN'
>>> nltk.corpus.brown.tagged_words() [('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ...]
Exploring tagged corpora
• Ch.5, p. 184-189
OTHER POS-TAGGED CORPORA
• NLTK:• WAC Corpora:
– English: UKWAC– Italian: ITWAC
POS TAGGING
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Markov Model POS tagging
• Again, the problem is to find an `explanation’ with the highest probability:
• As in the lecture on text classification, this can be ‘turned around’ using Bayes’ Rule:
)..|..(argmax 11Tt
nn wwttPi
)..(
)..()..|..(argmax
1
111
n
nnn
wwP
ttPttwwP
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Combining frequency and contextual information
• As in the case of spelling, this equation can be simplified:
• As we will see, once further simplifications are applied, this equation will encode both FREQUENCY and CONTEXT INFORMATION
prior
1
likelihood
11 )..()..|..(argmax nnn ttPttwwP
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Three further assumptions
• MARKOV assumption: a tag only depends on a FIXED NUMBER of previous tags (here, assume bigrams)– Simplify second factor
• INDEPENDENCE assumption: words are independent from each other.
• A word’s identity only depends on its own tag– Simplify first factor
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The final equations
FREQUENCYCONTEXT
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Estimating the probabilities
Can be done using Maximum Likelihood Estimation as usual, for BOTH probabilities:
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An example of tagging with Markov Models :
• Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN
• People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN
• Problem: assign a tag to race given the subsequences– to/TO race/???– the/DT race/???
• Solution: we choose the tag that has the greater of these probabilities:– P(VB|TO) P(race|VB)– P(NN|TO)P(race|NN)
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Tagging with MMs (2)• Actual estimates from the Switchboard corpus:• LEXICAL FREQUENCIES:
– P(race|NN) = .00041– P(race|VB) = .00003
• CONTEXT:– P(NN|TO) = .021– P(VB|TO) = .34
• The probabilities:– P(VB|TO) P(race|VB) = .00001– P(NN|TO)P(race|NN) = .000007
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A graphical interpretation of the POS tagging equations
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Hidden Markov Models
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An example
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Computing the most likely sequence of tags
• In general, the problem of computing the most likely sequence t1 .. tn could have exponential complexity
• It can however be solved in polynomial time using an example of DYNAMIC PROGRAMMING: the VITERBI ALGORITHM (Viterbi, 1967)
• (Also called TRELLIS ALGORITHMs)
POS TAGGING IN NLTK
DEFAULT POS TAGGER: nltk.pos_tag
>>> text = nltk.word_tokenize("And now for something completely different")>>> nltk.pos_tag(text) [('And', 'CC'), ('now', 'RB'), ('for', 'IN'), ('something', 'NN'), ('completely', 'RB'), ('different', 'JJ')]
TEXTPRO
• The most widely used NLP tool for Italian• http://textpro.fbk.eu/• Demo
THE TEXTPRO TAGSET
READINGS
• Bird et al, chapter 5, chapter 6.1
