José Camacho-Collados

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The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources , such as WordNet, or on encyclope-dic resources, such as Wikipedia. We propose a vector(More)
Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot be used effectively for the representation of individual word senses. We put forward a novel multilingual concept representation , called MUFFIN, which not only enables accurate representation of(More)
Despite being one of the most popular tasks in lexical semantics, word similarity has often been limited to the English language. Other languages, even those that are widely spoken such as Span-ish, do not have a reliable word similarity evaluation framework. We put forward robust methodologies for the extension of existing English datasets to other(More)
Annotation sémantique et validation terminologique en texte intégral en SHS Résumé. Nos travaux se focalisent sur la validation d'occurrences de candidats termes en contexte. Les contextes d'occurrences proviennent d'articles scientifiques des sciences du langage issus du corpus SCIENTEXT 1. Les candidats termes sont identifiés par l'extracteur automatique(More)
Lexical taxonomies are graph-like hierarchical structures that provide a formal representation of knowledge. Most knowledge graphs to date rely on is-a (hypernymic) relations as the backbone of their semantic structure. In this paper, we propose a supervised distributional framework for hypernym discovery which operates at the sense level, enabling(More)
Linking concepts and named entities to knowledge bases has become a crucial Natural Language Understanding task. In this respect, recent works have shown the key advantage of exploiting textual definitions in various Natural Language Processing applications. However, to date there are no reliable large-scale corpora of sense-annotated textual definitions(More)
Representing the semantics of linguistic items in a machine-interpretable form has been a major goal of Natural Language Processing since its earliest days. Among the range of different linguistic items, words have attracted the most research attention. However, word representations have an important limitation: they conflate different meanings of a word(More)
WordNet is probably the best known lexical resource in Natural Language Processing. While it is widely regarded as a high quality repository of concepts and semantic relations, updating and extending it manually is costly. One important type of relation which could potentially add enormous value to WordNet is the inclusion of collocational information,(More)
Word embeddings based on neural networks are widely used in Natural Language Processing. However, despite their success in capturing semantic information from massive corpora, word embed-dings still conflate different meanings of a word into a single vectorial representation and do not benefit from information available in lexical resources. We address this(More)