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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 -1 is returned. A score of 1 represents identity i.e. comparing a sense with itself will return 1.
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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.
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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). Note that at this time the scores given do _not_ always agree with those given by Pedersen's Perl implementation of Wordnet Similarity. 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.
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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). Note that at this time the scores given do _not_ always agree with those given by Pedersen's Perl implementation of Wordnet Similarity, although they are mostly very similar. The required IC values are precomputed and stored in a file, the name of which should be passed as the 'datafile' argument. For more information on how they are calculated, check brown_ic.py.
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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)). Note that at this time the scores given do _not_ always agree with those given by Pedersen's Perl implementation of Wordnet Similarity, although they are mostly very similar. The required IC values are calculated using precomputed frequency counts, which are accessed from the 'datafile' file which is supplied as an argument. For more information on how they are calculated, check brown_ic.py.
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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)). Note that at this time the scores given do _not_ always agree with those given by Pedersen's Perl implementation of Wordnet Similarity, although they are mostly very similar. The required IC values are calculated using precomputed frequency counts, which are accessed from the 'datafile' file which is supplied as an argument. For more information on how they are calculated, check brown_ic.py.
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Finds the least common subsumer of two synsets in a Wordnet taxonomy, where the least common subsumer is defined as the ancestor node common to both input synsets whose shortest path to the root node is the longest.
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Get the least common subsumer of the two input synsets, where the least common subsumer is defined as the ancestor synset common to both input synsets which has the highest information content (IC) value. The IC value is calculated by extracting the probability of an ancestor synset from a precomputed set.
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