in this area have many potential applications, such as information extraction, question answering, document summarization, machine translation, construction of thesauri and semantic networks, language modeling, paraphrasing, and recognizing textual entailment. In each of these potential applications, the contribution of the types of semantic analysis constitutes the most outstanding research issue. For example, in the word sense induction and disambiguation task, there are three separate phases: In the training phase, evaluation task participants were asked to use a training dataset to induce the sense inventories for a set of polysemous words. The training dataset consisting of a set of polysemous nouns/verbs and the sentence instances that they occurred in. No other resources were allowed other than morphological and syntactic Natural Language Processing components, such as morphological analyzers, Part-Of-Speech taggers and syntactic parsers. In the testing phase, participants were provided with a test set for the disambiguating subtask using the induced sense inventory from the training phase. In the evaluation phase, answers of to the testing phase were evaluated in a supervised an unsupervised framework. The unsupervised evaluation for WSI considered two types of evaluation V Measure (Rosenberg and Hirschberg, 2007), and paired F-Score (Artiles et al., 2009). This evaluation follows the supervised evaluation of SemEval-2007 WSI task (Agirre and Soroa, 2007) Senseval and SemEval tasks overview The tables below reflects the workshop growth from Senseval to SemEval and gives an overview of which area of computational semantics was evaluated throughout the Senseval/SemEval workshops. The Multilingual WSD task was introduced for the SemEval-2013 workshop. The task is aimed at evaluating Word Sense Disambiguation systems in a multilingual scenario using BabelNet as its sense inventory. Unlike similar task like crosslingual WSD or the multilingual lexical substitution task, where no fixed sense inventory is specified, Multilingual WSD uses the BabelNet as its sense inventory. Prior to the development of BabelNet, a bilingual lexical sample WSD evaluation task was carried out in SemEval-2007 on Chinese-English bitexts. The Cross-lingual WSD task was introduced in the SemEval-2007 evaluation workshop and re-proposed in the SemEval-2013 workshop . To facilitate the ease of integrating WSD systems into other Natural Language Processing (NLP) applications, such as Machine Translation and multilingual Information Retrieval, the cross-lingual WSD evaluation task was introduced a language-independent and knowledge-lean approach to WSD. The task is an unsupervised Word Sense Disambiguation task for English nouns by means of parallel corpora. It follows the lexical-sample variant of the