The gold standards used in the evaluation are hypernym taxonomies extracted from WordNet and GDP (Open Directory Project), and meronym taxonomies extracted from WordNet.

In total, there are 100 hypernym taxonomies, 50 each extracted from WordNet3 and ODP4, and 50 meronym taxonomies from WordNetS.

3 WordNet hypernym taxonomies are from 12 topics: gathering, professional, people, building, place, milk, meal, water, beverage, alcohol, dish, and herb.

(2004) extended isa relation acquisition towards terascale, and automatically identified hypernym patterns by minimal edit distance.

Hypernym Patterns Sibling Patterns

We have (11) Hypernym Patterns based on patterns proposed by (Hearst, 1992) and (Snow et al., 2005), (12) Sibling Patterns which are basically conjunctions, and (13) Part-of Patterns based on patterns proposed by (Girju et al., 2003) and (Cimiano and Wenderoth, 2007).

We addressed this problem by simply rerunning ASIA with a more specific class name (i.e., the first one returned); however, the result suggests that future work is needed to support automatic construction of hypernym hierarchy using semistructured web

For the experimental comparison, we focused on leaf semantic classes from the extended WordNet that have many hypernyms, so that a meaningful comparison could be made: specifically, we selected nouns that have at least three hypernyms, such that the hypernyms are the leaf nodes in the hypernym hierarchy of WordNet.

Preliminary experiments showed that (as in the experiments with Pasca’s classes above) ASIA did not always converge to the intended meaning; to avoid this problem, we instituted a second filter, and discarded ASIA’s results if the intersection of hypernyms from ASIA and WordNet constituted less than 50% of those in WordNet.

The opposite of hyponym is hypernym .

(2005) for exploiting WordNet to extract hypernym (isa) relations between entities, and is similar to the use of weakly labeled data in bioinfor-matics (Craven and Kumlien, 1999; Morgan et al.,

Approaches based on WordNet have often only looked at the hypernym (isa) or meronym (part-of) relation (Girju et al., 2003; Snow et al., 2005), while those based on the ACE program (Doddington et al., 2004) have been restricted in their evaluation to a small number of relation instances and corpora of less than a million words.

Hearst (1992) used a small number of regular expressions over words and part-of-speech tags to find examples of the hypernym relation.

A hyponymy-relation candidate is then extracted from the tree structure by regarding a node as a hypemym candidate and all its subordinate nodes as hyponym candidates of the hypernym candidate (e.g., (TIGER, TAXONOMY) and (TIGER, SIBERIAN TIGER) from Figure 4).

For example, “List of artists” is converted into “artists” by lexical pattern “list of Hyponymy-relation candidates whose hypernym candidate matches such a lexical pattern are likely to be valid (e.g., (List of artists, Leonardo da Vinci)).

In their approach, a common substring in a hypernym and a hyponym is assumed to be one strong clue for recognizing that the two words constitute a hyponymy relation.