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

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

Ontologies on the semantic web

As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The “Semantic Web” was touted by its developers as equally revolutionary but has not yet achieved anything like the Web’s exponential uptake. This 17 000 word survey article explores why this might be so, from a perspective that bridges both philosophy and IT

Simple Embedding-Based Word Sense Disambiguation

We present a simple knowledge-based WSD method that uses word and sense embeddings to compute the similarity between the gloss of a sense and the context of the word. Our method is inspired by the Lesk algorithm as it exploits both the context of the words and the definitions of the senses. It only requires large unlabeled corpora and a sense inventory such as WordNet, and therefore does not rely on annotated data. We explore whether additional extensions to Lesk are compatible with our method. The results of our experiments show that by lexically extending the amount of words in the gloss and context, although it works well for other implementations of Lesk, harms our method. Using a lexical selection method on the context words, on the other hand, improves it. The combination of our method with lexical selection enables our method to outperform state-of the art knowledge-based systems