A Baseline for NLP in Domain-Specific IR

Abstract

The information retrieval (IR) methods employed for the third participation of the University of Hagen in the domain-specific task of the Cross Language Evaluation Campaign (CLEF 2005) provide a baseline for experiments with natural language processing (NLP) methods in domain-specific IR than methods employed in our previous participations. The baseline consists of a combination of state-of-the-art IR methods with NLP methods for document and query processing.

Our monolingual experiments with German documents combine several methods to achieve better performance, including an entry vocabulary module (EVM), query expansion with semantically related concepts, and a blind feedback technique. The monolingual experiments focus on comparing two techniques for constructing database queries: creating a ‘bag of words’ and creating a semantic network by means of deep linguistic analysis of the query.

For the bilingual experiments, the English topics are translated into German queries with several machine translation (MT) services publicly available. Each set of translated topics is processed separately with the same techniques as in the monolingual experiments. Evaluation results for official experiments with a staged logistic regression and additional experiments with BM25 are presented.