type WordNetCorpusReader

Construct a new wordnet corpus reader, with the given root directory.

Parameters:

• root - A path pointer identifying the root directory for this corpus. If a string is specified, then it will be converted to a PathPointer automatically.
• fileids - A list of the files that make up this corpus. This list can either be specified explicitly, as a list of strings; or implicitly, as a regular expression over file paths. The absolute path for each file will be constructed by joining the reader's root to each file name.
• encoding - The default unicode encoding for the files that make up the corpus. encoding's value can be any of the following:
  • A string: encoding is the encoding name for all files.
  • A dictionary: encoding[file_id] is the encoding name for the file whose identifier is file_id. If file_id is not in encoding, then the file contents will be processed using non-unicode byte strings.
  • A list: encoding should be a list of (regexp, encoding) tuples. The encoding for a file whose identifier is file_id will be the encoding value for the first tuple whose regexp matches the file_id. If no tuple's regexp matches the file_id, the file contents will be processed using non-unicode byte strings.
  • None: the file contents of all files will be processed using non-unicode byte strings.
• tag_mapping_function - A function for normalizing or simplifying the POS tags returned by the tagged_words() or tagged_sents() methods.

Overrides: api.CorpusReader.__init__

Load all synsets with a given lemma and part of speech tag. If no pos is specified, all synsets for all parts of speech will be loaded.

Return all Lemma objects with a name matching the specified lemma name and part of speech tag. Matches any part of speech tag if none is specified.

Return all lemma names for all synsets for the given part of speech tag. If pos is not specified, all synsets for all parts of speech will be used.

Iterate over all synsets with a given part of speech tag. If no pos is specified, all synsets for all parts of speech will be loaded.

Path Distance Similarity: Return a score denoting how similar two word senses are, based on the shortest path that connects the senses in the is-a (hypernym/hypnoym) taxonomy. The score is in the range 0 to 1, except in those cases where a path cannot be found (will only be true for verbs as there are many distinct verb taxonomies), in which case None is returned. A score of 1 represents identity i.e. comparing a sense with itself will return 1.

Parameters:

• other (Synset) - The Synset that this Synset is being compared to.
• simulate_root (bool) - The various verb taxonomies do not share a single root which disallows this metric from working for synsets that are not connected. This flag (True by default) creates a fake root that connects all the taxonomies. Set it to false to disable this behavior. For the noun taxonomy, there is usually a default root except for WordNet version 1.6. If you are using wordnet 1.6, a fake root will be added for nouns as well.

Returns:

A score denoting the similarity of the two Synsets, normally between 0 and 1. None is returned if no connecting path could be found. 1 is returned if a Synset is compared with itself.

Leacock Chodorow Similarity: Return a score denoting how similar two word senses are, based on the shortest path that connects the senses (as above) and the maximum depth of the taxonomy in which the senses occur. The relationship is given as -log(p/2d) where p is the shortest path length and d is the taxonomy depth.

Parameters:

• other (Synset) - The Synset that this Synset is being compared to.
• simulate_root (bool) - The various verb taxonomies do not share a single root which disallows this metric from working for synsets that are not connected. This flag (True by default) creates a fake root that connects all the taxonomies. Set it to false to disable this behavior. For the noun taxonomy, there is usually a default root except for WordNet version 1.6. If you are using wordnet 1.6, a fake root will be added for nouns as well.

Returns:

A score denoting the similarity of the two Synsets, normally greater than 0. None is returned if no connecting path could be found. If a Synset is compared with itself, the maximum score is returned, which varies depending on the taxonomy depth.

Wu-Palmer Similarity: Return a score denoting how similar two word senses are, based on the depth of the two senses in the taxonomy and that of their Least Common Subsumer (most specific ancestor node). Previously, the scores computed by this implementation did _not_ always agree with those given by Pedersen's Perl implementation of WordNet Similarity. However, with the addition of the simulate_root flag (see below), the score for verbs now almost always agree but not always for nouns.

The LCS does not necessarily feature in the shortest path connecting the two senses, as it is by definition the common ancestor deepest in the taxonomy, not closest to the two senses. Typically, however, it will so feature. Where multiple candidates for the LCS exist, that whose shortest path to the root node is the longest will be selected. Where the LCS has multiple paths to the root, the longer path is used for the purposes of the calculation.

Parameters:

• other (Synset) - The Synset that this Synset is being compared to.
• simulate_root (bool) - The various verb taxonomies do not share a single root which disallows this metric from working for synsets that are not connected. This flag (True by default) creates a fake root that connects all the taxonomies. Set it to false to disable this behavior. For the noun taxonomy, there is usually a default root except for WordNet version 1.6. If you are using wordnet 1.6, a fake root will be added for nouns as well.

Returns:

A float score denoting the similarity of the two Synsets, normally greater than zero. If no connecting path between the two senses can be found, None is returned.

Resnik Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node).

Parameters:

• other (Synset) - The Synset that this Synset is being compared to.
• ic (dict) - an information content object (as returned by load_ic()).

Returns:

A float score denoting the similarity of the two Synsets. Synsets whose LCS is the root node of the taxonomy will have a score of 0 (e.g. N['dog'][0] and N['table'][0]).

Jiang-Conrath Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node) and that of the two input Synsets. The relationship is given by the equation 1 / (IC(s1) + IC(s2) - 2 * IC(lcs)).

Parameters:

• other (Synset) - The Synset that this Synset is being compared to.
• ic (dict) - an information content object (as returned by load_ic()).

Returns:

A float score denoting the similarity of the two Synsets.

Lin Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node) and that of the two input Synsets. The relationship is given by the equation 2 * IC(lcs) / (IC(s1) + IC(s2)).

Parameters:

• other (Synset) - The Synset that this Synset is being compared to.
• ic (dict) - an information content object (as returned by load_ic()).

Returns:

A float score denoting the similarity of the two Synsets, in the range 0 to 1.

Find a possible base form for the given form, with the given part of speech, by checking WordNet's list of exceptional forms, and by recursively stripping affixes for this part of speech until a form in WordNet is found.

>>> from nltk.corpus import wordnet as wn
>>> wn.morphy('dogs')
'dog'
>>> wn.morphy('churches')
'church'
>>> wn.morphy('aardwolves')
'aardwolf'
>>> wn.morphy('abaci')
'abacus'
>>> wn.morphy('hardrock', wn.ADV)
>>> wn.morphy('book', wn.NOUN)
'book'
>>> wn.morphy('book', wn.ADJ)

Creates an information content lookup dictionary from a corpus.

Parameters:

• corpus (CorpusReader) - The corpus from which we create an information content dictionary.
• weight_senses_equally (bool) - If this is True, gives all possible senses equal weight rather than dividing by the number of possible senses. (If a word has 3 synses, each sense gets 0.3333 per appearance when this is False, 1.0 when it is true.)
• smoothing (float) - How much do we smooth synset counts (default is 1.0)

Returns:

An information content dictionary

MORPHOLOGICAL_SUBSTITUTIONS

Value:

{'a': [('er', ''), ('est', ''), ('er', 'e'), ('est', 'e')], 'n': [('s', ''), ('ses', 's'), ('ves', 'f'), ('xes', 'x'), ('zes', 'z'), ('ches', 'ch'), ('shes', 'sh'), ...