The Robust-WSD at CLEF 2009 aims at exploring the contribution of Word Sense Disambiguation to monolingual and multilingual Information Retrieval. The organizers of the task provide documents and topics which have been automatically tagged with Word Senses from WordNet using several state-of-the-art Word Sense Disambiguation systems. The Robust-WSD exercise follows the same design as in 2008. I...

In this paper we present word sense disambiguation (WSD) experiments on ten highly polysemous verbs in Chinese, where significant performance improvements are achieved using rich linguistic features. Our system performs significantly better, and in some cases substantially better, than the baseline on all ten verbs. Our results also demonstrate that features extracted from the output of an auto...

Topic models can be used in an unsupervised domain adaptation for Word Sense Disambiguation (WSD). In the domain adaptation task, three types of topic models are available: (1) a topic model constructed from the source domain corpus: (2) a topic model constructed from the target domain corpus, and (3) a topic model constructed from both domains. Basically, three topic features made from each to...

We consider the problem of structural comparison of graphs with a focus on a particular dynamic graph, the Internet’s Autonomous System (AS) topology (§1.2). We develop theweighted spectral distribution (WSD), ametric basedon the distributionof a particular decomposition of a graph’s structure (§1.3) with a worked example (§1.4). We then turn to our particular application domain (§1.5), describ...

In this paper, we propose the Automatic Taxonomy Construction from Text (ATCT) framework for building taxonomies from text-based Web corpora. The framework is composed of multiple processing steps. Firstly, domain terms are extracted using a filtering method. Subsequently, Word Sense Disambiguation (WSD) is optionally applied in order to determine the senses of these terms. Then, by means of a ...

Word Sense Disambiguation (WSD) aims at determining the appropriate sense of a word in a particular context. Although it is a highly relevant task for Natural Language Processing, there are few works for Portuguese, which are tailored to specific applications, such as translation and information retrieval. In this work, we report our investigation of some general purpose WSD methods for nouns i...

Word Sense Disambiguation (WSD) is the Natural Language Processing (NLP) task that consists in selecting the correct sense of a polysemous word in a given context. Most state-of-the-art WSD systems are supervised classifiers that are trained on manually sense-tagged corpora, which are very time-consuming and expensive to build. In order to overcome this acquisition bottleneck (sense-tagged corp...

This paper explores the application of knowledgebased Word Sense Disambiguation systems to specific domains, based on our state-of-the-art graphbased WSD system that uses the information in WordNet. Evaluation was performed over a publicly available domain-specific dataset of 41 words related to Sports and Finance, comprising examples drawn from three corpora: one balanced corpus (BNC), and two...

In this paper, we present an analysis of feature extraction methods via dimensionality reduction for the task of biomedical Word Sense Disambiguation (WSD). We modify the vector representations in the 2-MRD WSD algorithm, and evaluate four dimensionality reduction methods: Word Embeddings using Continuous Bag of Words and Skip Gram, Singular Value Decomposition (SVD), and Principal Component An...

Word Sense Disambiguation (WSD) is one of the key issues in natural language processing. Currently, supervised WSD methods are effective ways to solve the ambiguity problem. However, due to lacking of large-scale training data, they cannot achieve satisfactory results. In this paper, we present a WSD method based on context translation. The method is based on the assumption that translation und...