The accuracy of current word sense disambiguation (WSD) systems is affected by the fine-grained sense inventory of WordNet as well as a lack of training examples. Using the WSD examples provided through OntoNotes, we conduct the first large-scale WSD evaluation involving hundreds of word types and tens of thousands of sense-tagged examples, while adopting a coarse-grained sense inventory. We sh...

We first reconsider the role of lexicographers in word-sense disambiguation as a computational task, as providers of both legacy material (dictionaries) and special test material for competitions like SENSEVAL. We suggest that the standard fine-grained division of senses and (larger) homographs by a lexicographer for use by a human reader may not be an appropriate goal for the computational WSD...

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation...

We present a novel almost-unsupervised approach to the task of Word Sense Disambiguation (WSD). We build sense examples automatically, using large quantities of Chinese text, and English-Chinese and Chinese-English bilingual dictionaries, taking advantage of the observation that mappings between words and meanings are often different in typologically distant languages. We train a classifier on ...

The paper addresses the issue of how to use linguistic information in Word Sense Disambiguation (WSD). We introduce a knowledge-driven and unsupervised WSD method that requires only a large corpus previously tagged with POS and very little grammatical knowledge. The WSD process is performed taking into account the syntactic patterns in which the ambiguous occurrence appears, relaying in the hyp...

This paper describes the automatic generation and the evaluation of sets of rules for word sense disambiguation (WSD) in machine translation. The ultimate aim is to identify high-quality rules that can be used as knowledge sources in a relational WSD model. The evaluation was carried out both automatically, by means of four objective measures (error, coverage, support and novelty), and manually...

To facilitate interaction and collaboration around ultrahigh-resolution, Wall-Size Displays (WSD), post-WIMP interaction modes like touchless and multi-touch have opened up new, unprecedented opportunities. Yet to fully harness this potential, we still need to understand fundamental design factors for successful WSD experiences. Some of these include visual feedback for touchless interactions, ...

Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports. To mine these data properly, attributable to their innate ambiguity, a Word Sense Disambiguation (WSD) algorithm can avoid numbers of difficulties in Natural Language Processing (NLP) pipeline. However, cons...

Mistranslation of an ambiguous word can have a large impact on the understandability of a given sentence. In this article, we describe a thorough evaluation of the translation quality of ambiguous nouns in three different setups. We compared two statistical Machine Translation systems and one dedicated Word Sense Disambiguation (WSD) system. Our WSD system incorporates multilingual information ...

This paper presents ongoing efforts on developing Word Sense Disambiguation (WSD) resources for the German language, using GermaNet as a basis. We bootstrap two WSD systems for German. (i) We enrich GermaNet with predominant sense information, following previous unsupervised methods to acquire predominant senses of words. The acquired predominant sense information is used as a type-based first ...