NLTK tagging. Basic tagging. Tagged corpora. POS tagging. NLTK tagging L435/L555. Dept. of Linguistics, Indiana University Fall / 18
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1 L435/L555 Dept. of Linguistics, Indiana University Fall / 18
2 Tagging We can use NLTK to perform a variety of NLP tasks Today, we will quickly cover the utilities for Other modules include: Classification Parsing, Chunking, & Grammar Writing Propositional Semantics & Logic Goal: make you comfortable learning more on your own 2 / 18
3 Segmentation & Tokenization As we saw, you can use nltk.word tokenize() to break a sentence into tokens nltk.sent tokenize breaks a text into sentences nltk.sent_tokenize("imagine me and you. I do. \ I think about you day and night.") [ Imagine me and you., I do., I think about you day and night. ] 3 / 18
4 Basic A very basic way to tag: >>> import nltk text = nltk.word_tokenize("they refuse to permit us to obtain the refuse permit") >>> nltk.pos_tag(text) [( They, PRP ), ( refuse, VBP ), ( to, TO ), ( permit, VB ), ( us, PRP ), ( to, TO ), ( obtain, VB ), ( the, DT ), ( refuse, NN ), ( permit, NN )] 4 / 18
5 Representing tagged tokens NLTK uses tuples to represent word, tag pairs: >>> tagged_token = nltk.tag.str2tuple( fly/nn ) >>> tagged_token ( fly, NN ) >>> >>> sent = They/PRP refuse/vbp to/to permit/vb us/prp to/to obtain/vb the/dt refuse/nn permit/nn >>> [nltk.tag.str2tuple(t) for t in sent.split()] [( They, PRP ), ( refuse, VBP ), ( to, TO ), ( permit, VB ), ( us, PRP ), ( to, TO ), ( obtain, VB ), ( the, DT ), ( refuse, NN ), ( permit, NN )] 5 / 18
6 Reading tagged corpora NLTK has tagged corpora to work with >>> nltk.corpus.brown.tagged_words() [( The, AT ), ( Fulton, NP-TL ),...] >>> nltk.corpus.brown.tagged_words(simplify_tags=true) [( The, DET ), ( Fulton, NP ), ( County, N ),...] 6 / 18
7 Corpus reading options Ways to access information for tagged corpora:.words() [list of words].tagged words() [list of (word,tag) pairs].sents() [list of list of words].tagged sents() [list of list of (word,tag) pairs].paras() [list of list of list of words].tagged paras() [list of list of list of (word,tag) pairs] 7 / 18
8 Calculating corpus statistics >>> from nltk.corpus import brown >>> brown_news_tagged = brown.tagged_words(categories= news, simplify_tags=true) >>> tag_fd = nltk.freqdist(tag for (word, tag) in brown_news_tagged) >>> tag_fd.keys() [ N, DET, P, NP, V, ADJ,,,., CNJ,...] >>> tag_fd[ N ] / 18
9 ConditionalFreqDist >>> wsj = nltk.corpus.treebank.tagged_words(simplify_tags=tru >>> cfd1 = nltk.conditionalfreqdist(wsj) >>> cfd1[ cut ].keys() [ V, VD, N, VN ] >>> cfd1[ cut ][ V ] 12 9 / 18
10 Automatic Most Frequent Tag Tagger >>> raw = I do not like green eggs and ham, I do not like them Sam I am! >>> tokens = nltk.word_tokenize(raw) >>> default_tagger = nltk.defaulttagger( NN ) >>> default_tagger.tag(tokens) [( I, NN ), ( do, NN ), ( not, NN ),...] >>> brown_tagged_sents = brown.tagged_sents(categories= news >>> default_tagger.evaluate(brown_tagged_sents) / 18
11 Automatic Regular Expression Tagger patterns = [... (r.*ing$, VBG ), # gerunds... (r.*ed$, VBD ), # simple past... (r.*es$, VBZ ), # 3rd singular pres... (r.*ould$, MD ), # modals... (r.*\ s$, NN$ ), # possessive nouns... (r.*s$, NNS ), # plural nouns... (r ˆ-?[0-9]+(.[0-9]+)?$, CD ), # cardinal numbers... (r.*, NN ) # nouns (default)... ] >>> regexp_tagger = nltk.regexptagger(patterns) Note that the patterns are applied in order 11 / 18
12 Automatic Regular Expression Tagger (2) >>> brown_sents = brown.sents(categories= news ) >>> regexp_tagger.tag(brown_sents[3]) [(, NN ),... ( such, NN ), ( reports, NNS ),... ( considering, VBG ), ( the, NN ),...] >>> >>> regexp_tagger.evaluate(brown_tagged_sents) / 18
13 Automatic Lookup Tagger Idea: use the most frequent tag for every word >>> fd = nltk.freqdist(brown.words(categories= news )) >>> cfd = nltk.conditionalfreqdist(brown.tagged_words(categor >>> most_freq_words = fd.keys()[:100] >>> likely_tags = dict((word, cfd[word].max()) for word in most_freq_words) >>> baseline_tagger = nltk.unigramtagger(model=likely_tags) >>> baseline_tagger.tag(brown.sents(categories= news )[3]) [(, ), ( Only, None), ( a, AT ),...] >>> baseline_tagger.evaluate(brown_tagged_sents) / 18
14 N-gram tagging Unigram tagging >>> unigram_tagger = nltk.unigramtagger(brown_tagged_sents) >>> unigram_tagger.tag(brown_sents[2007]) [( Various, JJ ), ( of, IN ), ( the, AT ),... ] >>> unigram_tagger.evaluate(brown_tagged_sents) / 18
15 N-gram tagging Training & Testing Data >>> size = int(len(brown_tagged_sents) * 0.9) >>> size 4160 >>> train_sents = brown_tagged_sents[:size] >>> test_sents = brown_tagged_sents[size:] >>> unigram_tagger = nltk.unigramtagger(train_sents) >>> unigram_tagger.evaluate(test_sents) / 18
16 N-gram tagging Bigram tagging >>> bigram_tagger = nltk.bigramtagger(train_sents) >>> bigram_tagger.tag(brown_sents[2007]) [( Various, JJ ), ( of, IN ), ( the, AT ),...] >>> unseen_sent = brown_sents[4203] >>> bigram_tagger.tag(unseen_sent) [( The, AT ), ( population, NN ), ( of, IN ),...] >>> bigram_tagger.evaluate(test_sents) / 18
17 N-gram tagging Combining taggers Use the best information if you have it: >>> t0 = nltk.defaulttagger( NN ) >>> t1 = nltk.unigramtagger(train_sents,backoff=t0) >>> t2 = nltk.bigramtagger(train_sents,backoff=t1) >>> t2.evaluate(test_sents) Unknown words can (also) be handled via regular expressions and be better integrated into contextual information 17 / 18
18 Exercises 10. Train a unigram tagger and run it on some new text. Observe that some words are not assigned a tag. Why not? 11. Learn about the affix tagger (type help(nltk.affixtagger)). Train an affix tagger and run it on some new text. Experiment with different settings for the affix length and the minimum word length. Discuss your findings. 18 / 18
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