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The quality of the material collected by a context-based Web search systems is highly dependant on the vocabulary used to generate the search queries. This paper proposes to apply a semi-supervised algorithm to incrementally learn terms that can help bridge the terminology gap existing between the user's information needs and the relevant documents' vocabulary. The learning strategy uses an incrementally-retrieved, topic-dependent selection of Web documents for term-weight reinforcement reflecting the aptness of the terms in describing and discriminating the topic of the user context. The new algorithm learns new descriptors by searching for terms that tend to occur often in relevant documents, and learns good discriminators by identifying terms that tend to occur only in the context of the given topic. The enriched vocabulary allows the formulation of search queries that are more effective than those queries generated directly using terms from the initial topic description. An evaluation on a large collection of topics using a standard and two ad-hoc performance evaluation metrics suggests that the proposed technique is superior to a baseline and other existing query reformulation techniques.