Word sense disambiguation (WSD) is the problem of deciding which sense a word has in any given context. The problem of doing WSD by computer is not new; it goes back to the early days of machine translation. But like other areas of computational linguistics, research into WSD has seen a resurgence because of the availability of large corpora. Statistical methods for WSD, especially techniques i...

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...

We conducted a 9-month prospective cohort study of 2,527 patients with systemic inflammatory response syndrome in three intensive care units and three general wards in a tertiary health care institution. Markov models were developed to predict the probability of movement to and from more severe stages--sepsis, severe sepsis, or septic shock--at 1, 3, and 7 days. For patients with sepsis, severe...