The proposed models are tested on three different tasks: coarse-grained word sense disambiguation, fine-grained word sense disambiguation, and detection of literal vs. nonliteral usages of potentially idiomatic expressions.

Sense Paraphrases For word sense disambiguation tasks, the paraphrases of the sense keys are represented by information from WordNet 2.1.

McCarthy (2009) also ad-lresses the issue of performance and cost by com-taring supervised word sense disambiguation sys-ems with unsupervised ones.

.‘he reason is that although this system is claimed 0 be unsupervised, and it performs better than .11 the participating systems (including the super-'ised systems) in the SemEval-2007 shared task, it till needs to incorporate a lot of prior knowledge, pecifically information about co-occurrences be-ween different word senses , which was obtained rom a number of resources (SSI+LKB) includ-ng: (i) SemCor (manually annotated); (ii) LDC-)SO (partly manually annotated); (iii) collocation lictionaries which are then disambiguated semi-.utomatically.

Table 4: Model performance (F-score) for the fine-grained word sense disambiguation task.

Word sense disambiguation (WSD) is the task of automatically determining the correct sense for a target word given the context in which it occurs.

There is a large body of work on WSD, covering supervised, unsupervised ( word sense induction) and knowledge-based approaches (see McCarthy (2009) for an overview).

Boyd-Graber and Blei (2007) propose an unsupervised approach that integrates McCarthy et al.’s (2004) method for finding predominant word senses into a topic modelling framework.

Topic models have also been applied to the related task of word sense induction.

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

However, this assumption does not hold, as the true distribution of word senses is often highly skewed (McCarthy, 2009).

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.

The final mapping contains 81,533 pairs of Wikipages and word senses they map to, covering 55.7% of the noun senses in WordNet.

Language Word senses Synsets

In Table 2 we report the number of synsets and word senses available in the gold-standard resources for the 5 languages.

We assess the coverage of BabelNet against our gold-standard wordnets both in terms of synsets and word senses .

Recent studies in the difficult task of Word Sense Disambiguation (Navigli, 2009b, WSD) have shown the impact of the amount and quality of lexical knowledge (Cuadros and Rigau, 2006): richer knowledge sources can be of great benefit to both knowledge-lean systems (Navigli and Lapata, 2010) and supervised classifiers (Ng and Lee, 1996; Yarowsky and Florian, 2002).

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.

Hereafter, we use word sense and synset interchangeably.

Given a WordNet word sense in our babel synset of interest (e.g.

Unlike some other thesauri (such as WordNet and thesaurus.com), entries are not broken down by word sense .

Unlike some other thesauri (including WordNet and thesaurus.com), the entries are not broken down by word sense or part of speech.

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

Also, our system is the first unsupervised method that has been applied to Erk and McCarthy’s (2009) graded word sense assignment task, showing a substantial positive correlation with the gold standard.

In this section, we apply our model to a different word sense ranking task: Given a word w in context, the task is to decide to what extent the different

(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.

In a second experiment, we apply our model to the “word sense similarity task” recently proposed by Erk and McCarthy (2009), which is a refined variant of a word-sense disambiguation task.

The objective is to incorporate (inverse) selectional preference information from the context (r, w’ ) in such a way as to identify the correct word sense of w. This suggests that the dimensions of should be filtered so that only those compatible with the context remain.

While pseudo-words originally evaluated word sense disambiguation, they are now commonly used to evaluate selectional preferences.

Pseudo-words were introduced simultaneously by two papers studying statistical approaches to word sense disambiguation (WSD).

One way to mitigate this problem is with pseudo-words, a method for automatically creating test corpora without human labeling, originally proposed for word sense disambiguation (Gale et al.,

While pseudo-words are now less often used for word sense disambigation, they are a common way to evaluate selectional preferences, models that measure the strength of association between a predicate and its argument filler, e.g., that the noun lunch is a likely object of eat.

We divide the algorithms into four categories based on their structural similarity: document-based, co-occurrence, approximation, and Word Sense Induction (WSI) models.

WSI models also use co-occurrence but also attempt to discover distinct word senses while building the vector space.

Word Sense Induction Models Purandare and Pedersen (Purandare and Pedersen, 2004) HERMIT (Jurgens and Stevens, 2010)

This idea originates from a similarity-based word sense disambiguation method developed by Karov and Edelman (1998).

To reflect two distinct aspects of the phenomenon, metaphor annotation can be split into two stages: identifying metaphorical senses in text (akin word sense disambiguation) and annotating source — target domain mappings underlying the production of metaphorical expressions.

Such annotation can be viewed as a form of word sense disambiguation with an emphasis on metaphoricity.