Syntax versus semantics: Analysis of enriched vector space models

Abstract. This paper presents a robust method for the construction of collection-specic document models. These document models are variants of the well-known vector space model, which relies on a pro-cess of selecting, modifying, and weighting index terms with respect to a given document collection. We improve the step of index term selection by applying statistical methods for concept identication. This approach is particularly suited for post-retrieval categorization and retrieval tasks in closed collections, which is typical for intranet search. We compare our approach to enriched vector-space-based doc-ument models that employ knowledge of the underlying language in the form of external semantic concepts. Primary objective is to quan-tify the impact of a purely syntactic analysis in contrast to a semantic enrichment in the index construction step. As a by-product we pro-vide an efcient and language-independent means for vector space model construction, whereas the resulting document models perform better than the standard vector space model