Query In Context (QIC) is a personalized search system that enhances individual search by incorporating user preferences in query expansion, capturing meanings embedded in documents, and ranking search results with context-enriched features. In this paper, we propose a new technique for QIC’s Query Expansion module, which reformulates user queries by using novel statistical-based and knowledge-based query expansion techniques to improve the returned results. The promising preliminary results analyzed through precision and recall metrics show better alignment between the user’s interests and the results retrieved.
In this paper, we report on indexing performance by a stateof-the-art keyphrase indexer, Maui, when paired with a text extraction procedure called text denoising. Text denoising is a method that extracts the denoised text, comprising the content-rich sentences, from full texts. The performance of the keyphrase indexer is demonstrated on three standard corpora collected from three domains, namely food and agriculture, high energy physics, and biomedical science. Maui is trained using the full texts and denoised texts. The indexer, using its trained models, then extracts keyphrases from test sets comprising full texts, and their denoised and noise parts (i.e., the part of texts that remains after denoising). Experimental findings show that against a gold standard, the denoised-text-trained indexer indexing full texts, performs either better than or as good as its benchmark performance produced by a full-text-trained indexer indexing full texts.