| Resumen |
Traditional concept retrieval is based on usual word definition dictionaries with simple performance: they just map words to their definitions. This approach is mostly helpful for readers and language students, but writers sometimes need to find a word that encompasses a set of ideas that they have in mind. For this task, inverse dictionaries are ready to help; however, in some cases a sought word does not correspond to a single definition but to a composite meaning of several concepts. A language producer then tends to require a concept search that starts with a group of words or a series of related terms, looking for a target word. This paper aims to assist on this task by presenting a new approach for concept blending through the development of a search-by-concept method based on vector space representation using semantic analysis and statistical natural language processing techniques. Words are represented as numeric vectors based on different semantic similarity measures and probabilistic measures; the semantic properties of a word are captured in the vector elements determined by a given linguistic context. Three different sources are used as context for word vector construction: WordNet, a distributional thesaurus, and the Latent Dirichlet Allocation algorithm; each source is used for building a different semantic vector space. |
