Experiment results show that the similarity function based on synset definitions is most effective.

First, the similarity function based on synset definitions is the most effective one.

As shown in Table 2, the similarity function based on synset definitions, i.e., sdef, is most effective.

We find that the most effective way to utilize the information from WordNet is to compute the term similarity based on the overlap of synset definitions.

Every node in the WordNet is a synset , i.e., a set of synonyms.

The definition of a synset , which is referred to as gloss, is also provided.

For a query term, all the synsets in which the term appears can be returned, along with the definition of the synsets .

tween the two extremes of full synsets and SFs.

As mentioned above, words in WordNet are organised into sets of synonyms, called synsets .

Each synset in turn belongs to a unique semantic file (SF).

We experiment with both full synsets and SFs as instances of fine-grained and coarse-grained semantic representation, respectively.

Our choice for this work was the WordNet 2.1 lexical database, in which synonyms are grouped into synsets , which are then linked via an ISA hierarchy.

With any lexical semantic resource, we have to be careful to choose the appropriate level of granularity for a given task: if we limit ourselves to synsets we will not be able to capture broader gen-eralisations, such as the one between knife and scissors;1 on the other hand by grouping words related at a higher level in the hierarchy we could find that we make overly coarse groupings (e.g.

1In WordNet 2.1, knife and scissors are sister synsets , both of which have TOOL as their 4th hypernym.

In this case, synsets slightly outperform SF.

We consider 10 measures, noted in the table as J&C (Jiang and Conrath, 1997), Resnik (Resnik, 1995), Lin (Lin, 1998), W&P (Wu and Palmer, 1994), L&C (Leacock and Chodorow, 1998), H&SO (Hirst and St—Onge, 1998), Path (counts edges between synsets ), Lesk (Banerjee and Pedersen, 2002), and finally Vector and Vector Pair (Patwardhan, 2003).

We mean a concept in Roget’s to be either a Class, Section, ..., Semicolon Group, while a concept in WordNet is any synset .

Likewise, in WordNet if c were a synset, then each Ci would be a hyponym synset of 0.