Antonymy and Conceptual Vectors

Antonymy and Conceptual Vectors Didier Schwab, Mathieu Lafourcade and Violaine Prince LIRMM Laboratoire d’informatique, de Robotique et de Micro´electronique de Montpellier MONTPELLIER - FRANCE. {schwab,lafourca,prince}@lirmm.fr http://www.lirmm.fr/ ˜{schwab, lafourca, prince} Abstract For meaning representations in NLP, we focus our attention on thematic aspects and concep- tual vectors. The learning strategy of concep- tual vectors relies on a morphosyntaxic analy- sis of human usage dictionary definitions linked to vector propagation. This analysis currently doesn’t take into account negation phenomena. This work aims at studying the antonymy as- pects of negation, in the larger goal of its inte- gration into the thematic analysis. We present a model based on the idea of symmetry compat- ible with conceptual vectors. Then, we define antonymy functions which allows the construc- tion of an antonymous vector and the enumer- ation of its potentially antinomic lexical items. Finally, we introduce a measure which evaluates how a given word is an acceptable antonym for a term. 1 Introduction Research in meaning representation in NLP is an important problem still addressed through several approaches. The NLP team at LIRMM currently works on thematic and lexical disam- biguation text analysis (Laf01). Therefore we built a system, with automated learning capa- bilities, based on conceptual vectors for mean- ing representation. Vectors are supposed to en- code ‘ideas’ associated to words or to expres- sions. The conceptual vectors learning system automatically defines or revises its vectors ac- cording to the following procedure. It takes, as an input, definitions in natural language con- tained in electronic dictionaries for human us- age. These definitions are then fed to a morpho- syntactic parser that provides tagging and anal- ysis trees. Trees are then used as an input to a procedure that computes vectors using tree geometry and syntactic functions. Thus, a kernel of manually indexed terms is necessary for bootstrapping the analysis. The transver- sal relationships1, such as synonymy (LP01), antonymy and hyperonymy, that are more or less explicitly mentioned in definitions can be used as a way to globally increase the coher- ence of vectors. In this paper, we describe a vectorial function of antonymy. This can help to improve the learning system by dealing with negation and antonym tags, as they are often present in definition texts. The antonymy func- tion can also help to find an opposite thema to be used in all generative text applications: op- posite ideas research, paraphrase (by negation of the antonym), summary, etc. 2 Conceptual Vectors We represent thematic aspects of textual seg- ments (documents, paragraph, syntagms, etc) by conceptual vectors. Vectors have been used in information retrieval for long (SM83) and for meaning representation by the LSI model (DDL+90) from latent semantic analysis (LSA) studies in psycholinguistics. In computational linguistics, (Cha90) proposes a formalism for the projection of the linguistic notion of se- mantic field in a vectorial space, from which our model is inspired. From a set of elemen- tary concepts, it is possible to build vectors (conceptual vectors) and to associate them to lexical items2. …

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