Finding predominant word senses in untagged text

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of handtagged data. Whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. We present work on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically. The acquired predominant senses give a precision of 64% on the nouns of the SENSEVAL- 2 English all-words task. This is a very promising result given that our method does not require any hand-tagged text, such as SemCor. Furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domainspecific corpora

A proposal for a shallow ontologization of WordNet

En este artículo se presenta el trabajo que se está realizando para la llamada ontologización superficial de WordNet, una estructura orientada a superar muchos de los problemas estructurales de la popular base de conocimiento léxico. El resultado esperado es un recurso multilingüe más apropiado que los ahora existentes para el procesamiento semántico a gran escala.This paper presents the work carried out towards the so-called shallow ontologization of WordNet, which is argued to be a way to overcome most of the many structural problems of the widely used lexical knowledge base. The result shall be a multilingual resource more suitable for large-scale semantic processing

Retrieving with good sense

Although always present in text, word sense ambiguity only recently became regarded as a problem to information retrieval which was potentially solvable. The growth of interest in word senses resulted from new directions taken in disambiguation research. This paper first outlines this research and surveys the resulting efforts in information retrieval. Although the majority of attempts to improve retrieval effectiveness were unsuccessful, much was learnt from the research. Most notably a notion of under what circumstance disambiguation may prove of use to retrieval

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

A descriptive study about Wordnet (MCR) and linguistics synsets

Este artigo apresenta o trabalho realizado para aplicar a WordNet MCR ao domínio linguístico e discute as situações problemáticas geradas pela estrutura WordNet e pelas características inerentes ao domínio. Foi empregado o enfoque descritivo para explicar como a manutenção da estrutura original da WordNet pode afetar as extensões de um domínio específico. Nossos resultados mostram que, para poder ampliar os synsets de domínios específicos, é inevitável uma reorganização estrutural