Ioannis P. Klapaftis

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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, and then disambiguate unseen instances of the same words using the(More)
Most work on word sense disambiguation has assumed that word usages are best labeled with a single sense. However, contextual ambiguity or fine-grained senses can potentially enable multiple sense interpretations of a usage. We present a new SemEval task for evaluating Word Sense Induction and Disambiguation systems in a setting where instances may be(More)
Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extraction and others. Most of the work in these areas formulate their problem in a graph-based setting and apply unsupervised graph clustering to obtain a set of clusters. Recent studies suggest(More)
This paper is an outcome of ongoing research and presents an unsupervised method for automatic word sense induction (WSI) and disambiguation (WSD). The induction algorithm is based on modeling the cooccurrences of two or more words using hypergraphs. WSI takes place by detecting high-density components in the cooccurrence hypergraphs. WSD assigns to each(More)
This paper presents the evaluation setting for the SemEval-2010 Word Sense Induction (WSI) task. The setting of the SemEval-2007 WSI task consists of two evaluation schemes, i.e. unsupervised evaluation and supervised evaluation. The first one evaluates WSI methods in a similar fashion to Information Retrieval exercises using F-Score. However, F-Score(More)
Compositional Distributional Semantic methods model the distributional behavior of a compound word by exploiting the distributional behavior of its constituent words. In this setting, a constituent word is typically represented by a feature vector conflating all the senses of that word. However, not all the senses of a constituent word are relevant when(More)
Automatic Term Recognition (ATR) is defined as the task of identifying domain specific terms from technical corpora. Termhoodbased approaches measure the degree that a candidate term refers to a domain specific concept. Unithood-based approaches measure the attachment strength of a candidate term constituents. These methods have been evaluated using(More)