Categorizing and Tagging Words
Chapter 5 of the NLTK book
Plan for tonight• Quiz• Part of speech tagging– Use of the Python dictionary data type– Application of regular expressions
• Planning for the rest of the semester
Understanding text• “Understanding” written and spoken text is
a very complicated process– Distinguishing characteristic of humans– Machines do not understand
• Trying to make machines behave as though they understand has led to interesting insights into the nature of understanding in humans
• Humans acquire an ability to characterize aspects of language as used in an instance, which aids in seeing meaning in the text or speech.
• If we want machines to process autonomously, we have to provide explicitly the extra information that humans learn to append to what is written or spoken.
Categorizing and Tagging Words
• Chapter goals: address these questions:– What are lexical categories and how are they
used in natural language processing?– What is a good Python data structure for
storing words and their categories?– How can we automatically tag each word of a
text with its word class?• This necessarily introduces some of the
fundamental concepts of natural language processing. – Potential use in many application areas
Word classes• aka Lexical categories.• The particular collection of tags used is the
tagset– How nice it would be if there were only one
tagset, general enough for all use!• Tags for parts of speech identify nouns, verbs,
adverbs, adjectives, articles, etc– Verbs are further tagged with tense, passive or
active voice, etc.– There are lots of possibilities for the kinds of
tagging desired.
First tagging example
• First, tokenize• The tags are cryptic. In this case,– CC = coordinating conjunction– RB = adverb– IN = preposition– NN = noun– JJ = adjective
>>> 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')]
Example
import nltk# from nltk.book import *raw = raw_input("Enter a sentence: ")text = nltk.word_tokenize(raw)print textresult = nltk.pos_tag(text)print result
Enter a sentence: And now for something completely different
['And', 'now', 'for', 'something', 'completely', 'different']
[('And', 'CC'), ('now', 'RB'), ('for', 'IN'), ('something', 'NN'), ('completely', 'RB'), ('different', 'JJ')]
Homonyms• Different words that are spelled the
same, but have different meanings– They may be pronounced differently, or not– Tags cannot be assigned to the word
independently. Word in context required.
More difficult exampleimport nltk# from nltk.book import *raw = raw_input("Enter a sentence: ")text = nltk.word_tokenize(raw)print textresult = nltk.pos_tag(text)print result
Enter a sentence: They refuse to permit us to obtain the refuse permit.['They', 'refuse', 'to', 'permit', 'us', 'to', 'obtain', 'the', 'refuse', 'permit', '.'][('They', 'PRP'), ('refuse', 'VBP'), ('to', 'TO'), ('permit', 'VB'), ('us', 'PRP'), ('to', 'TO'), ('obtain', 'VB'), ('the', 'DT'), ('refuse', 'NN'), ('permit', 'NN'), ('.', '.')]
What use is tagging?• The goal is autonomous processing that
correctly communicates in understandable language.– Results: • automated telephone trees• Spoken directions in gps• Directions provided by mapping programs• Any system that uses free text input and
provides appropriate information responses– shopping assistants, perhaps– Medical diagnosis systems– Many more
Spot check
• Describe a context in which program “understanding” of free text is needed and/or in which text or spoken response – that was not preprogrammed – is useful
Tagged Corpora• In nltk, tagged token is a tuple – (token, tag)– This allows us to isolate the two components
and use each easily• Tags in some corpora are done differently,
conversion available >>> tagged_token = nltk.tag.str2tuple('fly/NN')>>> tagged_token('fly', 'NN')>>> tagged_token[0]'fly'>>> tagged_token[1]'NN'
Steps to token, tag tuples• Original text has each word followed by / and the
tag. – Change to tokens.
• Each word and / and tag is a token
– Separate each token into a word, tag tuple>>> sent = '''... The/AT grand/JJ jury/NN commented/VBD on/IN a/AT number/NN of/IN... other/AP topics/NNS ,/, among/IN them/PPO the/AT Atlanta/NP and/CC... Fulton/NP-tl County/NN-tl purchasing/VBG departments/NNS which/WDT it/PPS... said/VBD ``/`` zr\\are/BER well/QL operated/VBN and/CC follow/VB generally/RB... accepted/VBN practices/NNS which/WDT inure/VB to/IN the/AT best/JJT... interest/NN of/IN both/ABX governments/NNS ''/'' ./.... '''>>> [nltk.tag.str2tuple(t) for t in sent.split()][('The', 'AT'), ('grand', 'JJ'), ('jury', 'NN'), ('commented', 'VBD'),('on', 'IN'), ('a', 'AT'), ('number', 'NN'), ... ('.', '.')
Handling differing tagging styles
• Different corpora have different conventions for tagging.
• NLTK harmonizes those and presents them all as tuples– tagged_words() method available for all
tagged text in the corpus.• Note this will probably not work for arbitrarily
selected files. This is done for the files in the corpus.
• NLTK simplified tagset
Simplified Tagset of NLTK
Other languages• NLTK is used for languages other than
English, and for alphabets and writing forms other than the Western characters.
• See the example showing Four Indian Languages (and tell me if they look meaningful!)
Looking at text for part of speech>>> 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', 'P', 'DET', 'NP', 'V', 'ADJ', ',', '.', 'CNJ', 'PRO', 'ADV', 'VD', ...]Key
Patterns for nouns• noun tags – N for common nouns, NP for
proper nouns• What parts of speech occur before a noun?– Construct list of bigrams• word-tag pairs
– Frequency Distribution>>> word_tag_pairs = nltk.bigrams(brown_news_tagged)
>>> list(nltk.FreqDist(a[1] for (a, b) in word_tag_pairs if b[1] == 'N'))
['DET', 'ADJ', 'N', 'P', 'NP', 'NUM', 'V', 'PRO', 'CNJ', '.', ',', 'VG', 'VN', ...]
A closer look
list(nltk.FreqDist(a[1] for (a, b) in word_tag_pairs if b[1] == 'N'))
>>> word_tag_pairs = nltk.bigrams(brown_news_tagged)>>> list(nltk.FreqDist(a[1] for (a, b) in word_tag_pairs if b[1] == 'N'))['DET', 'ADJ', 'N', 'P', 'NP', 'NUM', 'V', 'PRO', 'CNJ', '.', ',', 'VG', 'VN', ...]
Let’s parse that carefully. What is in word_tag_pairs?
>>> word_tag_pairs[10](("Atlanta's", 'NP'), ('recent', 'ADJ'))
>>> word_tag_pairs[10:15][(("Atlanta's", 'NP'), ('recent', 'ADJ')), (('recent', 'ADJ'), ('primary', 'N')), (('primary', 'N'), ('election', 'N')), (('election', 'N'), ('produced', 'VD')), (('produced', 'VD'), ('``', '``'))]
One bigram (Atlanta’s recent) showing each word with its tag
>>> word_tag_pairs[10][1]('recent', 'ADJ')
>>> word_tag_pairs[10][1][0]'recent’
A slice of the word_tag_pairs with red parentheses bracketing each tuple)
Patterns for verbs• Looking for verbs in the news text and
sorting by frequency:>>> wsj = nltk.corpus.treebank.tagged_words(simplify_tags=True)>>> word_tag_fd = nltk.FreqDist(wsj)>>> [word + "/" + tag for (word, tag) in word_tag_fd if tag.startswith('V')]
['is/V', 'said/VD', 'was/VD', 'are/V', 'be/V', 'has/V', 'have/V', 'says/V','were/VD', 'had/VD', 'been/VN', "'s/V", 'do/V', 'say/V', 'make/V', 'did/VD','rose/VD', 'does/V', 'expected/VN', 'buy/V', 'take/V', 'get/V', 'sell/V','help/V', 'added/VD', 'including/VG', 'according/VG', 'made/VN', 'pay/V', ...]
for <…> in <frequency distribution> if <condition> -- format for conditional distribution
import nltk# from nltk.book import *wsj=nltk.corpus.treebank.tagged_words(simplify_tags=True)word_tag_fd = nltk.FreqDist(wsj)print "Frequency distribution:"print word_tag_fdverbs= [word+"/"+tag for (word,tag) in word_tag_fd if tag.startswith('V')]print "Verbs: "print verbs[:25]
Frequency distribution:<FreqDist: (',', ','): 4885, ('the', 'DET'): 4038, ('.', '.'): 3828, ('of', 'P'): 2319, ('to', 'TO'): 2161, ('a', 'DET'): 1874, ('in', 'P'): 1554, ('and', 'CNJ'): 1505, ('*-1', ''): 1123, ('0', ''): 1099, ...>Verbs: ['is/V', 'said/VD', 'are/V', 'was/VD', 'be/V', 'has/V', 'have/V', 'says/V', 'were/VD', 'had/VD', 'been/VN', "'s/V", 'do/V', 'say/V', 'make/V', 'did/VD', 'rose/VD', 'does/V', 'expected/VN', 'buy/V', 'take/V', 'get/V', 'sell/V', 'help/V', 'added/VD']
Conditional distribution
• Recall that conditional distribution requires an event and a condition. – We can treat the word as the condition and the tag as
the event. >>> cfd1 = nltk.ConditionalFreqDist(wsj)>>> cfd1['yield'].keys()['V', 'N']>>> cfd1['cut'].keys()['V', 'VD', 'N', 'VN']
import nltkwsj=nltk.corpus.treebank.tagged_words(simplify_tags=True)cfd1=nltk.ConditionalFreqDist(wsj)word = raw_input("Enter the word to explore: ")if word in cfd1: wordkeys = cfd1[word].keys() print wordkeyselse: print "Word not found"
For yield and cut, show the parts of speech.
Distributions
• or reverse, so the words are the events and we see the tags commonly associated with given words:
>>> cfd2 = nltk.ConditionalFreqDist((tag, word) for\ (word, tag) in wsj)>>> cfd2['VN'].keys()['been', 'expected', 'made', 'compared', 'based', 'priced', 'used', 'sold','named', 'designed', 'held', 'fined', 'taken', 'paid', 'traded', 'said', ...]
Verb tense• Clarifying past tense and past participle,
look at some words that are the same for both and the words that are surrounding them: >>> [w for w in cfd1.conditions() if 'VD' in cfd1[w] and 'VN' in cfd1[w]]['Asked', 'accelerated', 'accepted', 'accused', 'acquired', 'added', 'adopted', ...]>>> idx1 = wsj.index(('kicked', 'VD'))>>> wsj[idx1-4:idx1+1][('While', 'P'), ('program', 'N'), ('trades', 'N'), ('swiftly', 'ADV'),('kicked', 'VD')]>>> idx2 = wsj.index(('kicked', 'VN'))>>> wsj[idx2-4:idx2+1][('head', 'N'), ('of', 'P'), ('state', 'N'), ('has', 'V'), ('kicked', 'VN')]
Spot check• Get the list of past participles found with
cfd2[‘VN’].keys()• Collect all the context for each by
showing word-tag pair immediately before and immediately after each.
>>> cfd2 = nltk.ConditionalFreqDist((tag, word) for\ (word, tag) in wsj)
Adjectives and Adverbs• … and other parts of speech. We can
find them all, examine their context, etc.
Example, looking at a word• “often” how is it used in common
English usage?>>> brown_learned_text = brown.words(categories='learned')>>> sorted(set(b for (a, b) in nltk.ibigrams(brown_learned_text) if a == 'often'))[',', '.', 'accomplished', 'analytically', 'appear', 'apt', 'associated', 'assuming','became', 'become', 'been', 'began', 'call', 'called', 'carefully', 'chose', ...]
This gives us a set of sorted words, from the bigrams in the indicated text, where the first word is “often”
>>> brown_lrnd_tagged = brown.tagged_words(categories='learned', simplify_tags=True)>>> tags = [b[1] for (a, b) in nltk.ibigrams(brown_lrnd_tagged) if a[0] == 'often']>>> fd = nltk.FreqDist(tags)>>> fd.tabulate() VN V VD DET ADJ ADV P CNJ , TO VG WH VBZ . 15 12 8 5 5 4 4 3 3 1 1 1 1 1
This gives us the distribution of the part of speech of the word following “often” in sorted order
Finding patterns
• We saw how to use regular expressions to extract patterns on words, now let’s extract patterns of parts of speech.– We look at each three-word phrase in the text, and find <verb>
to <verb>:
from nltk.corpus import browndef process(sentence): for (w1,t1), (w2,t2), (w3,t3) in \ nltk.trigrams(sentence): if (t1.startswith('V') and t2 == 'TO' and \ t3.startswith('V')): print w1, w2, w3 >>> for tagged_sent in brown.tagged_sents():... process(tagged_sent)...combined to achievecontinue to placeserve to protectwanted to waitallowed to placeexpected to become...
POS ambiguities• Looking at how the words are tagged,
may help understand the tagging>>> brown_news_tagged = brown.tagged_words(categories='news',\ simplify_tags=True)>>> data = nltk.ConditionalFreqDist((word.lower(), tag)... for (word, tag) in brown_news_tagged)>>> for word in data.conditions():... if len(data[word]) > 3:... tags = data[word].keys()... print word, ' '.join(tags)...best ADJ ADV NP Vbetter ADJ ADV V DETclose ADV ADJ V Ncut V N VN VDeven ADV DET ADJ V
Recall that a Conditional Frequency Distribution (ConditionalFreqDist()has an event and a condition.Each element of data has data.conditions() and each condition has keysdata[…].keys()
Python Dictionary
• Allows mapping between arbitrary types (not necessary to have a numeric index)
• Note the addition of defaultdict– returns a value for a non-existing entry• uses the default value for the type• 0 for number, [] for empty list, etc.
– We can specify a default value to use
Tagging rare words with default value
>>> alice = nltk.corpus.gutenberg.words('carroll-alice.txt')>>> vocab = nltk.FreqDist(alice)>>> v1000 = list(vocab)[:1000]>>> mapping = nltk.defaultdict(lambda: 'UNK')>>> for v in v1000:... mapping[v] = v...>>> alice2 = [mapping[v] for v in alice]>>> alice2[:100]['UNK', 'Alice', "'", 's', 'Adventures', 'in', 'Wonderland', 'by', 'UNK', 'UNK','UNK', 'UNK', 'CHAPTER', 'I', '.', 'UNK', 'the', 'Rabbit', '-', 'UNK', 'Alice','was', 'beginning', 'to', 'get', 'very', 'tired', 'of', 'sitting', 'by', 'her',
A lambda function is an unnamed function that can be defined and used wherever a function object is required.
Spot check• Look at the previous example• For each line of the code, determine
exactly what it does. Think first, then insert print commands to show each result.
Incrementally updating a dictionary
• Initialize an empty defaultdict• Process each part of speech tag in the text• If it has not been seen before, it will have zero count• Each time the tag is seen, increment its counter
• itemgetter(n) returns a function that can be called on some other sequence object to obtain the nth element
>>> counts = nltk.defaultdict(int)>>> from nltk.corpus import brown>>> for (word, tag) in brown.tagged_words(categories='news'):... counts[tag] += 1...>>> counts['N']22226>>> list(counts)['FW', 'DET', 'WH', "''", 'VBZ', 'VB+PPO', "'", ')', 'ADJ', 'PRO', '*', '-', ...]
>>> from operator import itemgetter>>> sorted(counts.items(), key=itemgetter(1), reverse=True)[('N', 22226), ('P', 10845), ('DET', 10648), ('NP', 8336), ('V', 7313), ...]>>> [t for t, c in sorted(counts.items(), key=itemgetter(1), reverse=True)]['N', 'P', 'DET', 'NP', 'V', 'ADJ', ',', '.', 'CNJ', 'PRO', 'ADV', 'VD', ...]
Look at the parameters of sorted. What does each represent?
Wha
t to
sort
Wha
t is t
he so
rt ke
y
Wha
t ord
er
Another pattern for updatingimport nltklast_letters = nltk.defaultdict(list)words = nltk.corpus.words.words('en')for word in words: key = word[-2:] last_letters[key].append(word)print last_letters['ly']
['abactinally', 'abandonedly', 'abasedly', 'abashedly', 'abashlessly', 'abbreviately','abdominally', 'abhorrently', 'abidingly', 'abiogenetically', 'abiologically', ...]
Note that each entry in the dictionary has a unique keyThe value part of the entry is a list
index• Because accumulating a list of words is so
common, NLTK defines a defaultdict(list) – nltk.Index
>>> anagrams = nltk.Index((''.join(sorted(w)), w) for w in words)>>> anagrams['aeilnrt']['entrail', 'latrine', 'ratline', 'reliant', 'retinal', 'trenail']
What does this do? >>> anagrams = nltk.defaultdict(list)>>> for word in words:... key = ''.join(sorted(word))... anagrams[key].append(word)...>>> anagrams['aeilnrt']
Exactly the same as this:
Inverted dictionary
>>> pos.update({'cats': 'N', 'scratch': 'V', 'peacefully': 'ADV', 'old': 'ADJ'})>>> pos2 = nltk.defaultdict(list)>>> for key, value in pos.items():... pos2[value].append(key)...>>> pos2['ADV']['peacefully', 'furiously']
>>> pos = {'colorless': 'ADJ', 'ideas': 'N', 'sleep': 'V', 'furiously': 'ADV'}>>> pos2 = dict((value, key) for (key, value) in pos.items())>>> pos2['N']'ideas' pos is a dictionary by { … }
pos2 is a dictionary by dict( … )
>>> pos{'furiously': 'ADV', 'sleep': 'V', 'ideas': 'N', 'colorless': 'ADJ'}>>> pos2{'ADV': 'furiously', 'N': 'ideas', 'ADJ': 'colorless', 'V': 'sleep'}
Automatic tagging• Default– Tag each token with the most common tag.
>>> tags = [tag for (word, tag) in brown.tagged_words(categories='news')]>>> nltk.FreqDist(tags).max()'NN'
>>> 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'), ('like', 'NN'), ('green', 'NN'),('eggs', 'NN'), ('and', 'NN'), ('ham', 'NN'), (',', 'NN'), ('I', 'NN'),('do', 'NN'), ('not', 'NN'), ('like', 'NN'), ('them', 'NN'), ('Sam', 'NN'),('I', 'NN'), ('am', 'NN'), ('!', 'NN')]
>>> default_tagger.evaluate(brown_tagged_sents)0.13089484257215028
Not very good!
NN (noun) is the most common tag in the given text.
NLTK includes an evaluate function for each tagger
Regular expression tagger• Use expected patterns to assign tags>>> patterns = [... (r'.*ing$', 'VBG’), # gerunds... (r'.*ed$', 'VBD'), # simple past... (r'.*es$', 'VBZ'), # 3rd singular present... (r'.*ould$', 'MD'), # modals... (r'.*\'s$', 'NN$'), # possessive nouns... (r'.*s$', 'NNS'), # plural nouns... (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), # cardinal num.... (r'.*', 'NN') # nouns (default)... ] >>> regexp_tagger = nltk.RegexpTagger(patterns)>>> regexp_tagger.tag(brown_sents[3])[('``', 'NN'), ('Only', 'NN'), ('a', 'NN'), ('relative', 'NN'), ('handful', 'NN'),('of', 'NN'), ('such', 'NN'), ('reports', 'NNS'), ('was', 'NNS'), ('received', 'VBD'),("''", 'NN'), (',', 'NN'), ('the', 'NN'), ('jury', 'NN'), ('said', 'NN'), (',', 'NN'),('``', 'NN'), ('considering', 'VBG'), ('the', 'NN'), ('widespread', 'NN'), ...]>>> regexp_tagger.evaluate(brown_tagged_sents)0.20326391789486245 better
Tag applied as soon as a match is found. If no match, defaults to NN
Lookup tagger• Find the most common words and store
their usual tag.
>>> fd = nltk.FreqDist(brown.words(categories='news'))>>> cfd = nltk.ConditionalFreqDist(brown.tagged_words(categories='news'))>>> 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.evaluate(brown_tagged_sents)0.45578495136941344
Still better
Look at this carefully
Refined lookup• Assign tags to words that are not nouns,
and default others to noun.
>>> baseline_tagger = nltk.UnigramTagger(model=likely_tags,... backoff=nltk.DefaultTagger('NN'))
Model size and performance
Evaluation• Gold standard test data– Corpus that has been manually annotated
and carefully evaluated. – Test the tagging technique against the test
case, where the right answers are known. If it does well there, assume it does well in general.
N-gram tagging• Unigram
– Use the most frequent tag for a word– Must have a “gold standard” for reference
>>> from nltk.corpus import brown>>> brown_tagged_sents = brown.tagged_sents(categories='news')>>> brown_sents = brown.sents(categories='news')
>>> unigram_tagger = nltk.UnigramTagger(brown_tagged_sents)>>> unigram_tagger.tag(brown_sents[2007])
[('Various', 'JJ'), ('of', 'IN'), ('the', 'AT'), ('apartments', 'NNS'),('are', 'BER'), ('of', 'IN'), ('the', 'AT'), ('terrace', 'NN'), ('type', 'NN'),(',', ','), ('being', 'BEG'), ('on', 'IN'), ('the', 'AT'), ('ground', 'NN'),('floor', 'NN'), ('so', 'QL'), ('that', 'CS'), ('entrance', 'NN'), ('is', 'BEZ'),('direct', 'JJ'), ('.', '.')]>>> unigram_tagger.evaluate(brown_tagged_sents)0.9349006503968017
Testing on the same data as training.
Separate training and testing
>>> size = int(len(brown_tagged_sents) * 0.9)>>> size4160>>> train_sents = brown_tagged_sents[:size]>>> test_sents = brown_tagged_sents[size:]>>> unigram_tagger = nltk.UnigramTagger(train_sents)>>> unigram_tagger.evaluate(test_sents)0.81202033290142528
The testing data is now different from the training data. So, this is a better test of the process.
If training and evaluation are on the same data, we certainly expect a very good performance!
More realistically, train on part of the data and test on the rest.
train on the last 4160 sentencestest on the rest of the sentences
Your turn• Experiment with this tagger.• Does it matter if you train on the first part or
the last part?• What is the effect of training on 80% of the
data and testing on the other 20%• Notice that the training and testing, though
on different sentences, is all from the same category of the Brown corpus. How well would you expect the training to do in a different corpus? If the corpus was also from a news category? If it was from a novel?
General N-Gram Tagging• Combine current word and the part of
speech tags of the previous n-1 words to give the current word some context.
Bigram tagger >>> bigram_tagger = nltk.BigramTagger(train_sents)>>> bigram_tagger.tag(brown_sents[2007])
[('Various', 'JJ'), ('of', 'IN'), ('the', 'AT'), ('apartments', 'NNS'),('are', 'BER'), ('of', 'IN'), ('the', 'AT'), ('terrace', 'NN'),('type', 'NN'), (',', ','), ('being', 'BEG'), ('on', 'IN'), ('the', 'AT'),('ground', 'NN'), ('floor', 'NN'), ('so', 'CS'), ('that', 'CS'),('entrance', 'NN'), ('is', 'BEZ'), ('direct', 'JJ'), ('.', '.')]
>>> unseen_sent = brown_sents[4203]>>> bigram_tagger.tag(unseen_sent)[('The', 'AT'), ('population', 'NN'), ('of', 'IN'), ('the', 'AT'), ('Congo', 'NP'),('is', 'BEZ'), ('13.5', None), ('million', None), (',', None), ('divided', None),('into', None), ('at', None), ('least', None), ('seven', None), ('major', None),('``', None), ('culture', None), ('clusters', None), ("''", None), ('and', None),('innumerable', None), ('tribes', None), ('speaking', None), ('400', None),('separate', None), ('dialects', None), ('.', None)] >>> bigram_tagger.evaluate(test_sents)0.10276088906608193 Reliance on context not seen in training reduces
accuracy.
This is the precision – recall tradeoff of information retrieval
So, what’s the problem?• Now, we are matching pairs of words and a
pair is less likely to have occurred before. • Using context provides greater accuracy,
when it is able to find a match. However, frequently, it will not find a match.
• Compromise – use the most accurate tagger for what it can do, then back it up with another tagger for the parts that do not get tagged.
Combining taggers• Use the benefits of several types of
taggers– Try the bigram tagger– When it is unable to find a tag, use the
unigram tagger– If that fails, then use the default tagger
>>> t0 = nltk.DefaultTagger('NN')>>> t1 = nltk.UnigramTagger(train_sents, backoff=t0)>>> t2 = nltk.BigramTagger(train_sents, backoff=t1)>>> t2.evaluate(test_sents)0.84491179108940495
Spot Check• Note the order of appearance of the
taggers. Why is that?• Extend to a trigramtagger, t3, which
backs off to t2.
Assignment for Chapter 5
• Assignment for two weeks:• NLTK chapter 5: # 3, 12 • Program: (be sure to work alone!) – Choose one of these: 5.25, 5.30, 5.33
