An overview of the SemEval-2 Japanese WSD task is presented. It is a lexical sample task, and word senses are defined according to a Japanese dictionary, the Iwanami Kokugo Jiten. This dictionary and a training corpus were distributed to participants. The number of target words was 50, with 22

In this paper we describe the SemEval-2010 Cross-Lingual Lexical Substitution task, where given an English target word in context, participating systems had to find an alternative substitute word or phrase in Spanish. The task is based on the English Lexical Substitution task run at SemEval-2007

In this paper we describe the SemEval-2010 Cross-Lingual Lexical Substitution task, where given an English target word in context, participating systems had to find an alternative substitute word or phrase in Spanish. The task is based on the English Lexical Substitution task run at SemEval-2007

We propose a multilingual unsupervised Word Sense Disambiguation (WSD) task for a sample of English nouns. Instead of providing manually sensetagged examples for each sense of a polysemous noun, our sense inventory is built up on the basis of the Europarl parallel corpus. The multilingual setup

The document describes the knowledgebased Domain-WSD system using heuristic rules (knowledge-base). This HR-WSD system delivered the best performance (55.9%) among all Chinese systems in SemEval-2010 Task 17: All-words WSD on a specific domain. 1

This paper presents the description and evaluation framework of SemEval-2010 Word Sense Induction & Disambiguation task, as well as the evaluation results of 26 participating systems. In this task, participants were required to induce the senses of 100 target words using a training set

This paper reports about our three par-ticipating systems in SemEval-2 Japanese WSD task. The first one is a clustering based method, which chooses a sense for, not individual instances, but automatically constructed clusters of instances. The sec-ond one is a classification method, which

Each year the Conference on Computational Natural Language Learning (CoNLL) 1 features a shared task, in which participants train and test their systems on exactly the same data sets, in order to better compare systems. The tenth CoNLL (CoNLL-X) saw a shared task on Multilingual Dependency Parsing

In this paper we describe the English Lexical Substitution task for SemEval. In the task, annotators and systems find an alternative substitute word or phrase for a target word in context. The task involves both finding the synonyms and disambiguating the context. Participating systems are free

tagged with WordNet 1.6 word senses. The organization provided training data in the form of a pre-processed Semcor which could be readily used by participants. The task had two participants, and the organizer also provided an in-house WSD system for comparison. The results do not improve over