Spotting and Discovering Terms through Natural Language Processing

Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR).

In this book Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR). Jacquemin's novel tool is FASTR, a parser that normalizes terms and recognizes term variants. Since there are more meanings in a language than there are words, FASTR uses a metagrammar composed of shallow linguistic transformations that describe the morphological, syntactic, semantic, and pragmatic variations of words and terms. The acquired parsed terms can then be applied for precise retrieval and assembly of information.

The use of a corpus-based unification grammar to define, recognize, and combine term variants from their base forms allows for intelligent information access to, or "linguistic data tuning" of, heterogeneous texts. FASTR can be used to do automatic controlled indexing, to carry out content-based Web searches through conceptually related alternative query formulations, to abstract scientific and technical extracts, and even to translate and collect terms from multilingual material. Jacquemin provides a comprehensive account of the method and implementation of this innovative retrieval technique for text processing.

Christian Jacquemin is Professor at the University of Paris 11 and Researcher in Computer Science at CNRS-LIMSI (Centre National de la Recherche Scientifique, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur).