We propose a mixed language query disambiguation approach by using co-occurrence information from monolingual data only. A mixed language query consists of words in a primary language and a secondary language. Our method translates the query into monolingual queries in either language. Two novel features for disambiguation, namely contextual word voting and 1-best contextual word, are introduced and compared to a baseline feature, the nearest neighbor. Average query translation accuracy for the two features are 81.37% and 83.72%, compared to the baseline accuracy of 75.50%.

In this paper, we present a prototype speech-based Web browser, SALSA1.0, and describe some of the research issues we need to address while building this system for Hong Kong users. SALSA1.0 allows the user to speak English command words as well as partial or complete link names on any page. The research issues involved in building SALSA1.0 are mainly (1) how to handle large accent variations and mixed-language and (2) how to handle unknown words, especially proper names, in Web links. The recognition engine for SALSA1.0 is trained on WSJ data, and then retrained on a small amount of Hong Kong accent WSJ data to handle accent variations. An edit-distance algorithm is used to replace all unknown words by the closest known word in the word network for recognition. With these methods, link name recognition rate is at 91.20% for links without unknown words, and 82.40% for links with unknown words. SALSA is currently being developed into a multilingual, natural language-based Intranet service provider for HKUST campus information access.