nltk.corpus.reader.wordnet.Synset

Undocumented

Undocumented

Return the transitive closure of source under the rel relationship, breadth-first, discarding cycles:

>>> from nltk.corpus import wordnet as wn
>>> computer = wn.synset('computer.n.01')
>>> topic = lambda s:s.topic_domains()
>>> print(list(computer.closure(topic)))
[Synset('computer_science.n.01')]

UserWarning: Discarded redundant search for Synset('computer.n.01') at depth 2

Include redundant pathes (but only once), avoiding duplicate searches (from 'animal.n.01' to 'entity.n.01'):

>>> dog = wn.synset('dog.n.01')
>>> hyp = lambda s:s.hypernyms()
>>> print(list(dog.closure(hyp)))
[Synset('canine.n.02'), Synset('domestic_animal.n.01'), Synset('carnivore.n.01'),
Synset('animal.n.01'), Synset('placental.n.01'), Synset('organism.n.01'),
Synset('mammal.n.01'), Synset('living_thing.n.01'), Synset('vertebrate.n.01'),
Synset('whole.n.02'), Synset('chordate.n.01'), Synset('object.n.01'),
Synset('physical_entity.n.01'), Synset('entity.n.01')]

UserWarning: Discarded redundant search for Synset('animal.n.01') at depth 7

Find all synsets that are hypernyms of this synset and the other synset.

Undocumented

Undocumented

Undocumented

Get the path(s) from this synset to the root, counting the distance of each node from the initial node on the way. A set of (synset, distance) tuples is returned.

Get the path(s) from this synset to the root, where each path is a list of the synset nodes traversed on the way to the root.

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)).

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.

Return all the lemma_names associated with the synset

Return all the lemma objects associated with the synset

Undocumented

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)).

Get a list of lowest synset(s) that both synsets have as a hypernym. When use_min_depth == False this means that the synset which appears as a hypernym of both self and other with the lowest maximum depth is returned or if there are multiple such synsets at the same depth they are all returned

However, if use_min_depth == True then the synset(s) which has/have the lowest minimum depth and appear(s) in both paths is/are returned.

By setting the use_min_depth flag to True, the behavior of NLTK2 can be preserved. This was changed in NLTK3 to give more accurate results in a small set of cases, generally with synsets concerning people. (eg: 'chef.n.01', 'fireman.n.01', etc.)

This method is an implementation of Ted Pedersen's "Lowest Common Subsumer" method from the Perl Wordnet module. It can return either "self" or "other" if they are a hypernym of the other.

synset to the root.

synset to the root.

Undocumented

Undocumented

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.

Undocumented

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).

Get the topmost hypernyms of this synset in WordNet.

Returns the distance of the shortest path linking the two synsets (if one exists). For each synset, all the ancestor nodes and their distances are recorded and compared. The ancestor node common to both synsets that can be reached with the minimum number of traversals is used. If no ancestor nodes are common, None is returned. If a node is compared with itself 0 is returned.

Return the full relation tree, including self, discarding cycles:

>>> from nltk.corpus import wordnet as wn
>>> from pprint import pprint
>>> computer = wn.synset('computer.n.01')
>>> topic = lambda s:s.topic_domains()
>>> pprint(computer.tree(topic))
[Synset('computer.n.01'), [Synset('computer_science.n.01')]]

UserWarning: Discarded redundant search for Synset('computer.n.01') at depth -3

But keep duplicate branches (from 'animal.n.01' to 'entity.n.01'):

>>> dog = wn.synset('dog.n.01')
>>> hyp = lambda s:s.hypernyms()
>>> pprint(dog.tree(hyp))
[Synset('dog.n.01'),
 [Synset('canine.n.02'),
  [Synset('carnivore.n.01'),
   [Synset('placental.n.01'),
    [Synset('mammal.n.01'),
     [Synset('vertebrate.n.01'),
      [Synset('chordate.n.01'),
       [Synset('animal.n.01'),
        [Synset('organism.n.01'),
         [Synset('living_thing.n.01'),
          [Synset('whole.n.02'),
           [Synset('object.n.01'),
            [Synset('physical_entity.n.01'),
             [Synset('entity.n.01')]]]]]]]]]]]]],
 [Synset('domestic_animal.n.01'),
  [Synset('animal.n.01'),
   [Synset('organism.n.01'),
    [Synset('living_thing.n.01'),
     [Synset('whole.n.02'),
      [Synset('object.n.01'),
       [Synset('physical_entity.n.01'), [Synset('entity.n.01')]]]]]]]]]

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.