Transformer-based architectures brought a breeze of change to Word Sense Disambiguation (WSD), improving models' performances by large margin. The fast development new approaches has been further encouraged well-framed evaluation suite for English, which allowed their be kept track and compared fairly. However, other languages have remained largely unexplored, as testing data are available few ...

In this Journal, Lewis and colleagues introduced a new Reliable Change Index (RCI(WSD)), which incorporated the within-subject standard deviation (WSD) of a repeated measurement design as the standard error. In this note, two opposite errors in using WSD this way are demonstrated. First, being the standard error of measurement of only a single assessment makes WSD too small when practice effect...

1. Aims Word Sense Disambiguation (WSD) involves the association of a polysemous word in a text or discourse with a particular sense among numerous potential senses of that word. In my thesis, we present a study of classifier combination and semi-supervised learning for WSD, which aim to boost supervised WSD and improve accuracy of WSD. In addition, we also work on context representation and fe...

This thesis shows that probabilistic word sense disambiguation systems based on established statistical methods are strong competitors to current state-of-the-art word sense disambiguation (WSD) systems. We begin with a survey of approaches to WSD, and examine their performance in the systems submitted to the Senseval-2 WSD evaluation exercise. We discuss existing resources for WSD, and investi...

This paper describes the participation of the REINA research group at CLEF 2009 Robust-WSD Task. We have participated in both monolingual and bilingual subtasks. In past editions of the robust task our research group obtained very good results for non-WSD experiments applying local query expansion using co-occurrence based thesauri constructed using windows of terms. We applied it again. For WS...

Many Word Sense Disambiguation (WSD) algorithms do not take into account the morphological variations in the language. However, as Indian languages are highly inflected languages, we investigate whether morphology must be taken into account for WSD for Indian languages, as they are very rich in morphology. This paper analyses the influence of morphology in WSD for Tamil. We believe our results ...

In this paper we study unsupervised word sense disambiguation (WSD) based on sense definition. We learn low-dimensional latent semantic vectors of concept definitions to construct a more robust sense similarity measure wmfvec. Experiments on four all-words WSD data sets show significant improvement over the baseline WSD systems and LDA based similarity measures, achieving results comparable to ...

BACKGROUND Genetic factors, a diet rich in fat and sugar, and an impaired intestinal barrier function are critical in the development of nonalcoholic steatohepatitis (NASH). The nonessential amino acid glutamine (Gln) has been suggested to have protective effects on intestinal barrier function but also against the development of liver diseases of various etiologies. OBJECTIVE The effect of or...

Word sense disambiguation (WSD) is described as the job of searching the sense of a word in a situation. WSD is a core problem in many tasks related to language processing. It is aggravated by make use of in several critical utilization like Part-of-Speech tagging, Machine Translation, Information retrieval, etc. Different topics such as ambiguity, evaluation, scalability and diversity cause ch...

The topic of a document can prove to be useful information for Word Sense Disambiguation (WSD) since certain meanings tend to be associated with particular topics. This paper presents an LDA-based approach for WSD, which is trained using any available WSD system to establish a sense per (Latent Dirichlet allocation based) topic. The technique is tested using three unsupervised and one supervise...