To obtain the paraphrases, we use the word forms, glosses and example sentences of the synset itself and a set of selected reference synsets (i.e., synsets linked to the target synset by specific semantic relations, see Table 1).

We excluded the ‘hypemym reference synsets’, since information common to all of the child synsets may confuse the disambiguation process.

In the latter case, each sense can be represented by its synset as well as its reference synsets .

We think that there are three reasons for this: first, adjectives and adverbs have fewer reference synsets for paraphrases compared with nouns and verbs (see Table 1); second, adjectives and adverbs tend to convey less key semantic content in the document, so they are more difficult to capture by the topic model; and third, adjectives and adverbs are a small portion of the test set, so their performances are statistically unstable.

MII+ref is the result of including the reference synsets , while MII-ref excludes the refer-

ence synsets .

Topics and synsets are then inferred together.

WordNet is a fairly rich resource which provides detailed information about word senses (glosses, example sentences, synsets , semantic relations between senses, etc.).

We collect (a) from WordNet, all available word senses (as concepts) and all the semantic pointers between synsets (as relations); (b) from Wikipedia, all encyclopedic entries (i.e.

We call the resulting set of multilingual lexicalizations of a given concept a babel synset .

A concept in WordNet is represented as a synonym set (called synset ), i.e.

For instance, the concept wind is expressed by the following synset:

We denote with w; the i-th sense of a word 7.0 with part of speech p. We use word senses to unambiguously denote the corresponding synsets (e.g.

Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms ( synsets ), each expressing a distinct concept (Miller, 1995).

Synsets are inter-linked by means of conceptual-semantic and lexical relations.

The simplest approach is to connect words that occur in the same WordNet synset .

The first baseline searches for each head noun in WordNet and labels the noun as category Ck, if it has a hypernym synset corresponding to that category.

We manually identified the WordNet synsets that, to the best of our ability, seem to most closely correspond

We do not report WordNet results for TEST because there did not seem be an appropriate synset , or for the OTHER category because that is a catchall class.

(2008), we represent different word senses by the words in the corresponding synsets .

For each word sense, we compute the centroid of the second-order vectors of its synset members.

Since synsets tend to be small (they even may contain only the target word itself), we additionally add the centroid of the sense’s hypernyms, scaled down by the factor 10 (chosen as a rough heuristic without any attempt at optimization).

It consists of a large number of synsets; a synset is a set of one or more similar word senses.

The synsets are then connected with hypemym/hyponym links, which represent ISA relationships.

The number of types of similarity in WordNet tends to be less than that captured by Moby, because synsets in WordNet are (usually) only allowed to have a single parent.