Abstract

The main obstacle for providing focused search is the relative opaqueness of search request—searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can leads to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the.GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search. Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model.

Citations