This paper describes our word sense disambiguation (WSD) system participating in the SemEval-2007 tasks. The core system is a fully supervised system based on a Naïve Bayes classifier using multiple knowledge sources. Toward a larger goal of incorporating the intrinsic nature of individual target words in disambiguation, thus introducing a cognitive element in automatic WSD, we tried to fine-tu...

Word Sense Disambiguation (WSD) is one of the toughest problems in NLP, and in WSD, verb disambiguation has proved to be extremely difficult, because of high degree of polysemy, too fine grained senses, absence of deep verb hierarchy and low inter annotator agreement in verb sense annotation. Unsupervised WSD has received widespread attention, but has performed poorly, specially on verbs. Recen...

Multi-Word Expressions (MWEs) are prevalent in text and are also, on average, less polysemous than mono-words. This suggests that accurate MWE detection should lead to a nontrivial improvement in Word Sense Disambiguation (WSD). We show that a straightforward MWE detection strategy, due to Arranz et al. (2005), can increase a WSD algorithm’s baseline f-measure by 5 percentage points. Our measur...

Resumen: En este art́ıculo presentamos una extensión de una arquitectura integrada diseñada originalmente para Semantic Parsing a WSD. El marco propuesto permitirá que ambas tareas puedan colaborar y llevarse a cabo simultáneamente. Se ha probado la validez y robustez de esta arquitectura contra una tarea de WSD bien definida (el SENSEVAL-II English Lexical Sample) aplicando modelos sintácticose...

We present a theoretically motivated method for creating probabilistic word sense disambiguation (WSD) systems. The method works by composing multiple probabilistic components: such modularity is made possible by an application of Bayesian statistics and Lidstone’s smoothing method. We show that a probabilistic WSD system created along these lines is a strong competitor to state-of-the-art WSD ...

Most word sense disambiguation (WSD) methods require large quantities of manually annotated training data and/or do not exploit fully the semantic relations of thesauri. We propose a new unsupervised WSD algorithm, which is based on generating Spreading Activation Networks (SANs) from the senses of a thesaurus and the relations between them. A new method of assigning weights to the networks’ li...

We present a system for for computer-aided WSD mark-up of texts in Spanish. The system is is based on Anaya dictionary, uses a Spanish morphological analyzer and a WSD method based on Lesk algorithm (along with the other standard strategies). This tool reduces time and effort for preparation WSD-marked corpora in Spanish. We also discuss the requirement for such type of systems, which our parti...

We would like to describe the relationship between word sense disambiguation (WSD) and language resources (LR) working with word senses. We discuss the problem of sense division and tagging. Exploiting specific features of the inflectional languages for WSD is encouraged. We present WSD methods for Czech ambiguous nouns. The advantage of these methods consists in reducing the manual work by usi...

BACKGROUND/OBJECTIVES Previous studies have indicated that when compared to young mice, old mice have lower global DNA methylation and higher p16 promoter methylation in colonic mucosa, which is a common finding in colon cancer. It is also known that a Western-style diet (WSD) high in fat and calories, and low in calcium, vitamin D, fiber, methionine and choline (based on the AIN 76A diet) is t...

This paper explores the application of structured learning methods (SLMs) to word sense disambiguation (WSD). On one hand, the semantic dependencies between polysemous words in the sentence can be encoded in SLMs. On the other hand, SLMs obtained significant achievements in natural language processing, and so it is a natural idea to apply them to WSD. However, there are many theoretical and pra...