Combining probabilistic and translation-based models for information retrieval based on word sense annotations

Abstract

The objective of our experiments in the monolingual robust word sense disambiguation (WSD) track at CLEF 2009 is twofold. On the one hand, we intend 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 aim at combining an often used probabilistic model, namely the Divergence From Randomness BM25 model, 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. © 2010 Springer-Verlag Berlin Heidelberg.