Our method is applied to the task of definition and hypernym extraction and compares favorably to other pattern generalization methods proposed in the literature.

A key feature of our approach is its inherent ability to both identify definitions and extract hypernyms .

WCLs are shown to generalize over lexico-syntactic patterns, and outperform well-known approaches to definition and hypernym extraction.

Hypernym Extraction.

The literature on hypernym extraction offers a higher variability of methods, from simple lexical patterns (Hearst, 1992; Oakes, 2005) to statistical and machine learning techniques (Agirre et al., 2000; Cara-ballo, 1999; Dolan et al., 1993; Sanfilippo and Poznanski, 1992; Ritter et al., 2009).

Finally, they train a hypernym clas-sifer based on these features.

o The DEFINIENS field (GF): it includes the genus phrase (usually including the hypernym , e.g., “a first-class function”);

For each sentence, the definiendum (that is, the word being defined) and its hypernym are marked in bold and italic, respectively.

Furthermore, in the final lattice, nodes associated with the hypernym words in the learning sentences are marked as hypernyms in order to be able to determine the hypernym of a test sentence at classification time.

The spin model approach uses word glosses, WordNet synonym, hypernym , and antonym relations, in addition to co-occurrence statistics extracted from corpus.

The proposed method achieves better performance by only using WordNet synonym, hypernym and similar to relations.

We build a network using only WordNet synonyms and hypernyms .

For example, we can use other WordNet relations: hypernyms , similar to,...etc.

Another local resource was WordNet where we inserted an edge (u, 2)) when U was a direct hypernym or synonym of u.

For each 75, E T with two variables and a single predicate word 21), we extract from WordNet the set H of direct hypernyms and synonyms of 21).

Negative examples are generated analogously, by looking at direct co-hyponyms of 212 instead of hypernyms and synonyms.

Combined with the constraint of transitivity this implies that there must be no path from u to v. This is done in the following two scenarios: (1) When two nodes u and v are identical except for a pair of words wu and my, and mu is an antonym of my, or a hypernym of my at distance 2 2.

We also intend to link missing concepts in WordNet, by establishing their most likely hypernyms — e.g., a la Snow et al.

0 Hypernymy/Hyponymy: all synonyms in the synsets H such that H is either a hypernym (i.e., a generalization) or a hyponym (i.e., a specialization) of S. For example, given bal-loo n},, we include the words from its hypernym { lighter-than-air craft}, } and all its hyponyms (e.g.

o Sisterhood: words from the sisters of S. A sister synset S’ is such that S and 8’ have a common direct hypernym .

To do so, we include words from their synsets, hypernyms , hyponyms, sisters, and glosses.

0 {Synonyms, Hypernyms } for all NN and VB entries for each word

Intersection of the words’ hypernyms

In fact, by themselves they proved roughly as useful as the hypernym features, and their removal had the single strongest negative impact on accuracy for our dataset.