Habanera is a multipurpose multilingual lexical knowledge base that is developed at CRL to be used as a central repository of multilingual lexical data. The knowledge base contains a set of dictionaries and relations between entries, within a dictionary (e.g., synonymy) as well as between entries of different dictionaries (e.g., translation). The format of monolingual lexical entries is left re...

This paper describes several experiments in text classification using WordNet, a rich source of lexical background knowledge available in the public domain. WordNet is used to map the original words from a text into sets based on synonym and hypernym relationships. This information is used to compute a change of representation from bag of words to hypernym density. Six binary classification tas...

In most natural language processing systems there is no representation of the semantic knowledge of lexical units, but just subcategorization frames, selection restrictions and links to other paradigmatically-related lexical units. Some NLP systems, e.g. machine translation or dialogue-based systems, attempt to “understand” the input text by translating it into some kind of formal language-inde...

The question of what is "purely linguistic" is considered in relation to the problem of modularity. A model is proposed in which parsing has access to world knowledge, and both contribute to the construction of a discourse model. The lexical semantic theory of naive semantics, which identifies word meanings with naive theories, and its use in computational text interpretation, demonstrate that ...

Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics. We propose a new learning objective that incorporates both a neural language model objective (Mikolov et al., 2013) and prior knowledge from semantic resources to learn improved lexical semantic embeddings. We demonstrate that our embeddings improve over those learned solely on ...

Lexical (and structural) ambiguities make language as expressive as it is. Computational lexicons thus have to cope with a large amount of polysemous words. Research in the last decade (e. g., Pustejovsky (1995), Kilgarriff and Gazdar (1995)) has aimed at identifying different types of polysemy in order to capture underlying regularities. This paper deals with a subtype of regular polysemy illu...

In this paper we present an API for programmatic access to BabelNet – a wide-coverage multilingual lexical knowledge base – and multilingual knowledge-rich Word Sense Disambiguation (WSD). Our aim is to provide the research community with easy-to-use tools to perform multilingual lexical semantic analysis and foster further research in this direction.

The French Lexical Network (fr-LN) is a global model of the French lexicon presently under construction. The fr-LN accounts for lexical knowledge as a lexical network structured by paradigmatic and syntagmatic relations holding between lexical units. This paper describes how morphological knowledge is presently being introduced into the fr-LN through the implementation and lexicographic exploit...

A Context Search algorithm used for lexical knowledge acquisition is presented. Knowledge representation based on psycholinguistic theories of cognitive processes allows for implementation of a computational model of semantic memory in the form of semantic network. Knowledge acquisition using supervised dialog templates have been performed in a word game designed to guess the concept a human us...