nltk.TextCollection¶
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class
nltk.TextCollection(source)[source]¶ A collection of texts, which can be loaded with list of texts, or with a corpus consisting of one or more texts, and which supports counting, concordancing, collocation discovery, etc. Initialize a TextCollection as follows:
>>> import nltk.corpus >>> from nltk.text import TextCollection >>> print('hack'); from nltk.book import text1, text2, text3 hack... >>> gutenberg = TextCollection(nltk.corpus.gutenberg) >>> mytexts = TextCollection([text1, text2, text3])
Iterating over a TextCollection produces all the tokens of all the texts in order.
Methods¶
__init__(source) |
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collocations([num, window_size]) |
Print collocations derived from the text, ignoring stopwords. | ||
common_contexts(words[, num]) |
Find contexts where the specified words appear; list most frequent common contexts first. | ||
concordance(word[, width, lines]) |
Print a concordance for word with the specified context window. |
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count(word) |
Count the number of times this word appears in the text. | ||
dispersion_plot(words) |
Produce a plot showing the distribution of the words through the text. | ||
findall(regexp) |
Find instances of the regular expression in the text. | ||
idf(term) |
The number of texts in the corpus divided by the number of texts that the term appears in. | ||
index(word) |
Find the index of the first occurrence of the word in the text. | ||
plot(*args) |
See documentation for FreqDist.plot() | ||
readability(method) |
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similar(word[, num]) |
Distributional similarity: find other words which appear in the same contexts as the specified word; list most similar words first. | ||
tf(term, text) |
The frequency of the term in text. | ||
tf_idf(term, text) |
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unicode_repr() |
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vocab() |
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