nltk.corpus.reader.wordnet

module documentation

(source)

An NLTK interface for WordNet

WordNet is a lexical database of English. Using synsets, helps find conceptual relationships between words such as hypernyms, hyponyms, synonyms, antonyms etc.

For details about WordNet see: http://wordnet.princeton.edu/

This module also allows you to find lemmas in languages other than English from the Open Multilingual Wordnet http://compling.hss.ntu.edu.sg/omw/

Class	`Lemma`	The lexical entry for a single morphological form of a sense-disambiguated word.
Class	`Synset`	Create a Synset from a "<lemma>.<pos>.<number>" string where: <lemma> is the word's morphological stem <pos> is one of the module attributes ADJ, ADJ_SAT, ADV, NOUN or VERB <number> is the sense number, counting from 0.
Exception	`WordNetError`	An exception class for wordnet-related errors.
Function	`information_content`	Undocumented
Function	`jcn_similarity`	Undocumented
Function	`lch_similarity`	Undocumented
Function	`lin_similarity`	Undocumented
Function	`path_similarity`	Undocumented
Function	`res_similarity`	Undocumented
Function	`wup_similarity`	Undocumented
Constant	`POS_LIST`	Undocumented
Constant	`SENSENUM_RE`	Undocumented
Constant	`VERB_FRAME_STRINGS`	Undocumented
Variable	`ADJ`	Undocumented
Variable	`ADJ_SAT`	Undocumented
Variable	`ADV`	Undocumented
Variable	`NOUN`	Undocumented
Variable	`VERB`	Undocumented
Class	`_WordNetObject`	A common base class for lemmas and synsets.
Function	`_get_pos`	Undocumented
Function	`_lcs_ic`	Get the information content of the least common subsumer that has the highest information content value. If two nodes have no explicit common subsumer, assume that they share an artificial root node that is the hypernym of all explicit roots.
Constant	`_INF`	Undocumented

def information_content(synset, ic): (source) ¶

Undocumented

def jcn_similarity(synset1, synset2, ic, verbose=False): (source) ¶

Undocumented

def lch_similarity(synset1, synset2, verbose=False, simulate_root=True): (source) ¶

Undocumented

def lin_similarity(synset1, synset2, ic, verbose=False): (source) ¶

Undocumented

def path_similarity(synset1, synset2, verbose=False, simulate_root=True): (source) ¶

Undocumented

def res_similarity(synset1, synset2, ic, verbose=False): (source) ¶

Undocumented

def wup_similarity(synset1, synset2, verbose=False, simulate_root=True): (source) ¶

Undocumented

POS_LIST = (source) ¶

Undocumented

Value

[NOUN, VERB, ADJ, ADV]

SENSENUM_RE = (source) ¶

Undocumented

Value

re.compile(r'\.\d+\.')

VERB_FRAME_STRINGS: tuple = (source) ¶

Undocumented

Value

(None,
 'Something %s',
 'Somebody %s',
 'It is %sing',
 'Something is %sing PP',
 'Something %s something Adjective/Noun',
 'Something %s Adjective/Noun',
...

ADJ = (source) ¶

Undocumented

ADJ_SAT = (source) ¶

Undocumented

ADV = (source) ¶

Undocumented

NOUN = (source) ¶

Undocumented

VERB = (source) ¶

Undocumented

def _get_pos(field): (source) ¶

Undocumented

def _lcs_ic(synset1, synset2, ic, verbose=False): (source) ¶

Get the information content of the least common subsumer that has the highest information content value. If two nodes have no explicit common subsumer, assume that they share an artificial root node that is the hypernym of all explicit roots.

speech as the first synset. :type ic: dict :param ic: an information content object (as returned by load_ic()). :return: The information content of the two synsets and their most informative subsumer

Parameters
synset1:Synset	First input synset.
synset2:Synset	Second input synset. Must be the same part of
ic	Undocumented
verbose	Undocumented

_INF: float = (source) ¶

Undocumented

Value

1e+300