Multilingual computational semantic lexicons in action: the WYSINNWYG approach to NLP

Much effort has been put into computational lexicons over the years, and most systems give much room to (lexical) semantic data. However, in these systems, the effort put on the study and representation of lexical items to express the underlying continuum existing in 1) language vagueness and polysemy, and 2) language gaps and mismatches, has remained embryonic. A sense enumeration approach fails from a theoretical point of view to capture the core meaning of words, let alone relate word meanings to one another, and complicates the task of NLP by multiplying ambiguities in analysis and choices in generation. In this paper, I study computational semantic lexicon representation from a multilingual point of view, reconciling different approaches to lexicon representation: i) vagueness for lexemes which have a more or less finer grained semantics with respect to other languages; ii) underspecification for lexemes which have multiple related facets; and, iii) lexical rules to relate systematic polysemy to systematic ambiguity. I build on a What You See Is Not Necessarily What You Get (WYSINNWYG) approach to provide the NLP system with the "right" lexical data already tuned towards a particular task. In order to do so, I argue for a lexical semantic approach to lexicon representation. I exemplify my study through a cross-linguistic investigation on spatially-based expressions.