`nltk.chunk`¶

Classes and interfaces for identifying non-overlapping linguistic groups (such as base noun phrases) in unrestricted text. This task is called “chunk parsing” or “chunking”, and the identified groups are called “chunks”. The chunked text is represented using a shallow tree called a “chunk structure.” A chunk structure is a tree containing tokens and chunks, where each chunk is a subtree containing only tokens. For example, the chunk structure for base noun phrase chunks in the sentence “I saw the big dog on the hill” is:

(SENTENCE:
  (NP: <I>)
  <saw>
  (NP: <the> <big> <dog>)
  <on>
  (NP: <the> <hill>))

To convert a chunk structure back to a list of tokens, simply use the chunk structure’s leaves() method.

This module defines ChunkParserI, a standard interface for chunking texts; and RegexpChunkParser, a regular-expression based implementation of that interface. It also defines ChunkScore, a utility class for scoring chunk parsers.

RegexpChunkParser¶

RegexpChunkParser is an implementation of the chunk parser interface that uses regular-expressions over tags to chunk a text. Its parse() method first constructs a ChunkString, which encodes a particular chunking of the input text. Initially, nothing is chunked. parse.RegexpChunkParser then applies a sequence of RegexpChunkRule rules to the ChunkString, each of which modifies the chunking that it encodes. Finally, the ChunkString is transformed back into a chunk structure, which is returned.

RegexpChunkParser can only be used to chunk a single kind of phrase. For example, you can use an RegexpChunkParser to chunk the noun phrases in a text, or the verb phrases in a text; but you can not use it to simultaneously chunk both noun phrases and verb phrases in the same text. (This is a limitation of RegexpChunkParser, not of chunk parsers in general.)

RegexpChunkRules¶

A RegexpChunkRule is a transformational rule that updates the chunking of a text by modifying its ChunkString. Each RegexpChunkRule defines the apply() method, which modifies the chunking encoded by a ChunkString. The RegexpChunkRule class itself can be used to implement any transformational rule based on regular expressions. There are also a number of subclasses, which can be used to implement simpler types of rules:

ChunkRule chunks anything that matches a given regular expression.

ChinkRule chinks anything that matches a given regular expression.

UnChunkRule will un-chunk any chunk that matches a given regular expression.

MergeRule can be used to merge two contiguous chunks.

SplitRule can be used to split a single chunk into two smaller chunks.

ExpandLeftRule will expand a chunk to incorporate new unchunked material on the left.

ExpandRightRule will expand a chunk to incorporate new unchunked material on the right.

Tag Patterns¶

A RegexpChunkRule uses a modified version of regular expression patterns, called “tag patterns”. Tag patterns are used to match sequences of tags. Examples of tag patterns are:

r'(<DT>|<JJ>|<NN>)+'
r'<NN>+'
r'<NN.*>'

The differences between regular expression patterns and tag patterns are:

In tag patterns, '<' and '>' act as parentheses; so '<NN>+' matches one or more repetitions of '<NN>', not '<NN' followed by one or more repetitions of '>'.

Whitespace in tag patterns is ignored. So '<DT> | <NN>' is equivalant to '<DT>|<NN>'

In tag patterns, '.' is equivalant to '[^{}<>]'; so '<NN.*>' matches any single tag starting with 'NN'.

The function tag_pattern2re_pattern can be used to transform a tag pattern to an equivalent regular expression pattern.

Efficiency¶

Preliminary tests indicate that RegexpChunkParser can chunk at a rate of about 300 tokens/second, with a moderately complex rule set.

There may be problems if RegexpChunkParser is used with more than 5,000 tokens at a time. In particular, evaluation of some regular expressions may cause the Python regular expression engine to exceed its maximum recursion depth. We have attempted to minimize these problems, but it is impossible to avoid them completely. We therefore recommend that you apply the chunk parser to a single sentence at a time.

Emacs Tip¶

If you evaluate the following elisp expression in emacs, it will colorize a ChunkString when you use an interactive python shell with emacs or xemacs (“C-c !”):

(let ()
  (defconst comint-mode-font-lock-keywords
    '(("<[^>]+>" 0 'font-lock-reference-face)
      ("[{}]" 0 'font-lock-function-name-face)))
  (add-hook 'comint-mode-hook (lambda () (turn-on-font-lock))))

You can evaluate this code by copying it to a temporary buffer, placing the cursor after the last close parenthesis, and typing “C-x C-e”. You should evaluate it before running the interactive session. The change will last until you close emacs.

Unresolved Issues¶

If we use the re module for regular expressions, Python’s regular expression engine generates “maximum recursion depth exceeded” errors when processing very large texts, even for regular expressions that should not require any recursion. We therefore use the pre module instead. But note that pre does not include Unicode support, so this module will not work with unicode strings. Note also that pre regular expressions are not quite as advanced as re ones (e.g., no leftward zero-length assertions).

type CHUNK_TAG_PATTERN:
	regexp
var CHUNK_TAG_PATTERN:
	A regular expression to test whether a tag pattern is valid.

Functions¶

`accuracy`(chunker, gold)	Score the accuracy of the chunker against the gold standard.
`conllstr2tree`(s[, chunk_types, root_label])	Return a chunk structure for a single sentence encoded in the given CONLL 2000 style string.
`conlltags2tree`(sentence[, chunk_types, ...])	Convert the CoNLL IOB format to a tree.
`ieerstr2tree`(s[, chunk_types, root_label])	Return a chunk structure containing the chunked tagged text that is encoded in the given IEER style string.
`load`(resource_url[, format, cache, verbose, ...])	Load a given resource from the NLTK data package.
`ne_chunk`(tagged_tokens[, binary])	Use NLTK’s currently recommended named entity chunker to chunk the given list of tagged tokens.
`ne_chunk_sents`(tagged_sentences[, binary])	Use NLTK’s currently recommended named entity chunker to chunk the given list of tagged sentences, each consisting of a list of tagged tokens.
`tagstr2tree`(s[, chunk_label, root_label, ...])	Divide a string of bracketted tagged text into chunks and unchunked tokens, and produce a Tree.
`tree2conllstr`(t)	Return a multiline string where each line contains a word, tag and IOB tag.
`tree2conlltags`(t)	Return a list of 3-tuples containing `(word, tag, IOB-tag)`.

Classes¶

`ChunkParserI`	A processing interface for identifying non-overlapping groups in unrestricted text.
`ChunkScore`(**kwargs)	A utility class for scoring chunk parsers.
`RegexpChunkParser`(rules[, chunk_label, ...])	A regular expression based chunk parser.
`RegexpParser`(grammar[, root_label, loop, trace])	A grammar based chunk parser.

nltk.chunk¶