Human beings use natural language to communicate with their pairs. They want to use it to communicate with machines as well. But because natural language is ambiguous by nature, Word Sense Disambiguation (WSD) is a crucial research topic for Human Language Technologies (see (Ide and Véronis 98)). WSD is necessary in most natural language applications (e.g. information retrieval, machine transla...

Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present new challenges. The difficulties found by supervised systems to adapt might change the way we assess the strengths and weaknesses of supervised and knowledgebased WSD systems. Unfortunately, all existing evaluation datasets for specific domains are lexical-sample corpora. With this paper we want to...

An approach to the word sense disambiguation (WSD) relaying on the WordNet synsets is proposed. The method uses semantically tagged glosses to perform a process similar to the spreading activation in semantic network, creating ranking of the most probable meanings for word annotation. Preliminary evaluation shows quite promising results. Comparison with the state-of-theart WSD methods indicates...

Many eye-tracking data analyses rely on the Area-of-Interest (AOI) methodology, which utilizes AOIs to analyze metrics such as fixations. However, AOI-based methods have some inherent limitations including variability and subjectivity in shape, size, location of AOIs. In this article, we propose an alternative approach traditional AOI dwell time analysis: Weighted Sum Durations (WSD). This decr...

In this paper we present an approach for the enrichment of WSD knowledge bases with data-driven relations from a gold standard corpus (annotated with word senses, valency information, syntactic analyses, etc.). We focus on Bulgarian as a use case, but our approach is scalable to other languages as well. For the purpose of exploring such methods, the Personalized Page Rank algorithm was used. Th...

The aim of this paper is describing the experiments, results achieved and further work, in a novel approach to the Word Sense Disambiguation(WSD) task. This novel approach consists, mainly, in the learning and combination of several semantic class classifiers. So we can not only get WSD systems with coarser granularity than the traditionally offered by WordNet senses, but also systems showing d...

In this paper, we discuss the importance of the quality against the quantity of automatically extracted examples for word sense disambiguation (WSD). We first show that we can build a competitive WSD system with a memory-based classifier and a feature set reduced to easily and efficiently computable features. We then show that adding automatically annotated examples improves the performance of ...

The application-driven construction of lexicon has been emphasized as a methodology of Computational Lexicology recently. We focus on the closed semantic constraint of the argument(s) of any verb concept by the noun concepts in a WordNet-like lexicon, which theoretically is related to Word Sense Disambiguation (WSD) at different levels. From the viewpoint of Dynamic Lexicon, WSD provides a way ...

In this demonstration we present SUPWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for ...

This paper presents a new approach for combining different semantic disambiguation methods that are part of a Word Sense Disambiguation(WSD) system. The way these methods are combined greatly influences the overall system performance. The approach is based on generating training examples, for each sense of the word, based on the output of each disambiguation method. A set of rules is learned fr...