Reading Tagged Corpora
Several of the corpora included with NLTK have been tagged for their part-of-speech. Here's an example of what you might see if you opened a file from the Brown Corpus with a text editor:
The/at Fulton/np-tl County/nn-tl Grand/jj-tl Jury/nn-tl said/vbd Friday/nr an/at inves-tigation/nn of/in Atlanta's/np$ recent/jj primary/nn election/nn produced/vbd / no/at evidence/nn ''/'' that/cs any/dti irregularities/nns took/vbd place/nn ./.
Other corpora use a variety of formats for storing part-of-speech tags. NLTK's corpus readers provide a uniform interface so that you don't have to be concerned with the different file formats. In contrast with the file extract just shown, the corpus reader for the Brown Corpus represents the data as shown next. Note that part-of-speech tags have been converted to uppercase; this has become standard practice since the Brown Corpus was published.
>>> nltk.corpus.brown.tagged_words()
[('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ...] >>> nltk.corpus.brown.tagged_words(simplify_tags=True) [('The', 'DET'), ('Fulton', 'N'), ('County', 'N'), ...]
Whenever a corpus contains tagged text, the NLTK corpus interface will have a tagged_words() method. Here are some more examples, again using the output format illustrated for the Brown Corpus:
>>> print nltk.corpus.nps_chat.tagged_words() [('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ...] >>> nltk.corpus.conll2000.tagged_words() [('Confidence', 'NN'), ('in', 'IN'), ('the', 'DT'), ...] >>> nltk.corpus.treebank.tagged_words() [('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ...]
Not all corpora employ the same set of tags; see the tagset help functionality and the readme() methods mentioned earlier for documentation. Initially we want to avoid the complications of these tagsets, so we use a built-in mapping to a simplified tagset:
>>> nltk.corpus.brown.tagged_words(simplify_tags=True) [('The', 'DET'), ('Fulton', 'NP'), ('County', 'N'), ...] >>> nltk.corpus.treebank.tagged_words(simplify_tags=True) [('Pierre', 'NP'), ('Vinken', 'NP'), (',', ','), ...]
Tagged corpora for several other languages are distributed with NLTK, including Chinese, Hindi, Portuguese, Spanish, Dutch, and Catalan. These usually contain nonASCII text, and Python always displays this in hexadecimal when printing a larger structure such as a list.
>>> nltk.corpus.sinica_treebank.tagged_words() [('\xe4\xb8\x80', 'Neu'), ('\xe5\x8f\x8b\xe6\x83\x85', 'Nad'), ...] >>> nltk.corpus.indian.tagged_words()
[('\xe0\xa6\xae\xe0\xa6\xb9\xe0\xa6\xbf\xe0\xa6\xb7\xe0\xa7\x87\xe0\xa6\xb0', 'NN'), ('\xe0\xa6\xb8\xe0\xa6\xa8\xe0\xa7\x8d\xe0\xa6\xa4\xe0\xa6\xbe\xe0\xa6\xa8', 'NN'), ... ]
>>> nltk.corpus.mac_morpho.tagged_words()
[('Jersei', 'N'), ('atinge', 'V'), ('m\xe9dia', 'N'), ...]
>>> nltk.corpus.conll2002.tagged_words()
[('Sao', 'NC'), ('Paulo', 'VMI'), ('(', 'Fpa'), ...]
>>> nltk.corpus.cess_cat.tagged_words()
[('El', 'da0ms0'), ('Tribunal_Suprem', 'np0000o'), ...]
If your environment is set up correctly, with appropriate editors and fonts, you should be able to display individual strings in a human-readable way. For example, Figure 5-1 shows data accessed using nltk.corpus.indian.
If the corpus is also segmented into sentences, it will have a tagged_sents() method that divides up the tagged words into sentences rather than presenting them as one big list. This will be useful when we come to developing automatic taggers, as they are trained and tested on lists of sentences, not words.
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