Cross-lingual Information Retrieval Using Hidden Markov Models

Cross-lingual Information Retrieval using Hidden Markov Models Jinxi Xu BBN Technologies 70 Fawcett St. Cambridge, MA, USA 02138 jxu@bbn.com Ralph Weischedel BBN Technologies 70 Fawcett St. Cambridge, MA, USA 02138 weischedel @bbn.com Abstract This paper presents empirical results in cross-lingual information retrieval using English queries to access Chinese documents (TREC-5 and TREC-6) and Spanish documents (TREC-4). Since our interest is in languages where resources may be minimal, we use an integrated probabilistic model that requires only a bilingual dictionary as a resource. We explore how a combined probability model of term translation and retrieval can reduce the effect of translation ambiguity. In addition, we estimate an upper bound on performance, if translation ambiguity were a solved problem. We also measure performance as a function of bilingual dictionary size. 1 Introduction Cross-language information retrieval (CLIR) can serve both those users with a smattering of knowledge of other languages and also those fluent in them. For those with limited knowledge of the other language(s), CLIR offers a wide pool of documents, even though the user does not have the skill to prepare a high quality query in the other language(s). Once documents are retrieved, machine translation or human translation, if desired, can make the documents usable. For the user who is fluent in two or more languages, even though he/she may be able to formulate good queries in each of the source languages, CLIR relieves the user from having to do so. Most CLIR studies have been based on a variant of tf-idf; our experiments instead use a hidden Markov model (HMM) to estimate the probability that a document is relevant given the query. We integrated two simple estimates of term translation probability into the mono- lingual HMM model, giving an estimate of the probability that a document is relevant given a query in another language. In this paper we address the following questions: • How can a combined probability model of term translation and retrieval minimize the effect of translation ambiguity? (Sections 3, 5, 6, 7, and 10) • What is the upper bound performance using bilingual dictionary lookup for term translation? (Section 8) • How much does performance degrade due to omissions from the bilingual dictionary and how does performance vary with size of such a dictionary? (Sections 8-9) All experiments were performed using a common baseline, an HMM-based (mono- lingual) indexing and retrieval engine. In order to design controlled experiments for the questions above, the IR system was run without sophisticated query expansion techniques. Our experiments are based on the Chinese materials of TREC-5 and TREC-6 and the Spanish materials of TREC-4. 2 HMM for Mono-Lingual Retrieval Following Miller et al., 1999, the IR system ranks documents according to the probability that a document D is relevant given the query Q, P(D is R IQ). Using Bayes Rule, and the fact that P(Q) is constant for a given query, and our initial assumption of a uniform a priori 95 probability that a document is relevant, ranking documents according to …

Content truncated to 500 words