Combining Probabilistic and Translation-Based Models for Information Retrieval based on Word Sense Annotations

In this paper, we describe our experiments carried out for the robust word sense disambiguation (WSD) track of the CLEF 2009 campaign. This track consists of a monolingual and bilingual task and addresses information retrieval utilizing word sense annotations. We took part in the monolingual task only. Our objective was twofold. On the one hand, we intended to increase the precision of WSD by a heuristic-based combination of the annotations of the two WSD systems. For this, we provide an extrinsic evaluation on different levels of word sense accuracy. On the other hand, we aimed at combining an often used probabilistic model, namely the Divergence From Randomness BM25 model (DFR BM25), with a monolingual translation-based model. Our best performing system with and without utilizing word senses ranked 1st overall in the monolingual task. However, we could not observe any improvement by applying the sense annotations compared to the retrieval settings based on tokens or lemmas only.