Most had around 550 senses ( synsets and their lemmas): for example, for Portuguese: Englishnzl inglés.

de Melo and Weikum (2009) also use this data (and data from a variety of other sources) to build an enhanced wordnet, in addition adding new synsets for concepts that are not in not wordnet.

Open class words (nouns, verbs, adjectives and adverbs) are grouped into concepts represented by sets of synonyms ( synsets ).

Synsets are linked by semantic relations such as hyponomy and meronomy.

The majority of freely available wordnets take the basic structure of the PWN and add new lemmas (words) to the existing synsets : the extend model (Vossen, 2005).

As our set C of semantic classes we selected the standard set of 3,299 core nominal synsets available in WordNet.8 However, our approach is flexible and can be used with classes of an arbitrary level of granularity.

As explained below, we assume the set C to be made up of representative synsets from WordNet.

This way we avoid building a new taxonomy and shift the problem to that of projecting the Wikipedia pages —associated with annotated filling arguments — to synsets in WordNet.

We exploit an existing mapping implemented in BabelNet (Navigli and Ponzetto, 2012), a wide-coverage multilingual semantic network that integrates Wikipedia and WordNet.3 Based on a disambiguation algorithm, BabelNet establishes a mapping ,u : Wikipages —> Synsets which links about 50,000 pages to their most suitable WordNet senses.4

The PPR measure (Agirre and Soroa, 2009) maps the glosses of the two senses to a semantic vector space spanned up by WordNet synsets and then compares them using the chi-square measure.

where M is a transition probability matrix between the n WordNet synsets , c is a damping factor, and vppr is a vector of size n representing the probability of jumping to the node 2' associated with each vi.

For personalized PageRank, vppr is initialized in a particular way: the initial weight is distributed equally over the m vector components (i.e., synsets ) associated with a word in the sense gloss, other components receive a 0 value.

(2008) map FrameNet frames to WordNet synsets based on the embedding of FrameNet lemmas in WordNet.

To create MapNet, Tonelli and Pianta (2009) align FrameNet senses with WordNet synsets by exploiting the textual similarity of their glosses.

The similarity measure is based on stem overlap of the candidates’ glosses expanded by WordNet domains, the WordNet synset , and the set of senses for a FrameNet frame.

A synonymous set of words in a WordNet is called a synset .

Each synset can be considered as a word cluster comprising of semantically similar words.

(2011) showed that WordNet synsets can act as good features for document level sentiment classification.

For example, on En-PD, percentage of features present in the test set and not present in the training set to those present in the test set are 34.17%, 11.24%, 0.31% for words, synsets

However, it must be noted that clustering based on unlabelled corpora is less taxing than manually creating paradigmatic property based clusters like WordNet synsets .

7.2 Effect of Synsets and Antonyms

We show the important effect of synsets and antonyms in computing the sentiment similarity of words.

This is indicates that the synsets of the words can improve the quality of the enriched matrix.

To compute the semantic similarity between word senses, we utilize their synsets as follows:

where, syn(w) is the synset of w. Let count(w,~, wi) be the co—occurrence of the w,- and w], and let count(w_,~) be the total word count.

In addition, note that employing the synset of the words help to obtain different emotional vectors for each sense of a word.

An element of a common concept hierarchy is defined as < sinid1,sinid2, ...,uwid,sum0id > where, sinidi is synset id of ith wordnet, uw _id is universal word id, and sumon is SUMO term id of the concept.

Each synset of wordnet is directly linked to a concept in ‘common concept hierarchy’.

ILI consists of English synsets and serves as a pivot to link other wordnets.

Because of the small size of the top level ontology, only a few wordnet synsets can be linked directly to the ontological concept and most of the synsets get linked through subsumption relation.

Distributional models that integrate the visual modality have been learned from texts and images (Feng and Lapata, 2010; Bruni et al., 2012b) or from ImageNet (Deng et al., 2009), e.g., by exploiting the fact that images in this database are hierarchically organized according to WordNet synsets (Leong and Mihalcea, 2011).

ImageNet has more than 14 million images spanning 21K WordNet synsets .

1Some words had to be modified in order to match the correct synset , e. g., tank_(c0ntainer) was found as storage_tank.