Context-sensitive information retrieval
Authors: Jing Bai
Venue:  (2007)

Information retrieval (IR) is more than ever an indispensable tool in today's society. With the growth of volume of available information, users' information needs have also much diversified. The same query can be used to specify very different information needs by different users. In such a situation, the query can no longer be taken as the only element that specifies the information need. Other contextual factors should be considered. However, the traditional approaches only use the query to retrieve documents. These approaches become insufficient. This study investigates the integration of contextual factors in IR operations. There have been many studies showing the importance of contextual factors in IR. However, few of them have been integrated in operational IR systems. In this study, several contextual factors are integrated within a uniform framework based on a language modeling approach. Each contextual factor is used to build a component query model that defines the information need from a different perspective. The final query model combines all the component query models, which make the query more complete. We consider a more complete query as a better specification of the information need, which allows us to retrieve more relevant documents. In this study, we distinguish two types of context for a query: (1) Intra-query context, which is the context specified by the terms included in the query. This type of context helps us to understand the meaning of query terms and to determine the appropriate expansion terms; (2) Extra-query context, which defines elements beyond the query itself and relates to the environment of the query. In this study, we investigate two of them: topic domain of the query which defines background information of the query; and the characteristics of the document collection which reveal the other topics that are developed together with the query topic in the collection. To exploit intra-query context, we create context-dependent term relations that determine related terms according to richer contexts. Two language models are created to model extra-query contexts: the domain model is defined by a set of in-domain documents, while the query-related collection characteristics are reflected by a set of feedback documents. We show in this study that both types of context are very useful. Our method has been tested on several TREC collections. Our experiments show that: (1) The integration of contextual factors is very useful in IR. Each of the contextual factors brings large improvements in retrieval effectiveness. The complete query model that integrates all the contextual factors performs the best; (2) The context-dependent term relations that exploit intra-query context can determine more relevant expansion terms than the traditional co-occurrence relations which do not exploit intra-query context; (3) The extra-query contexts can also bring large improvements in retrieval effectiveness. In particular, it is feasible to build topic domain models and use them to enhance the query. We have tested different strategies to build and to exploit the topic domain models. All the strategies have proven to be quite effective; (4) This study also shows that by exploiting pseudo feedback documents, we can capture some query-related collection characteristics. These characteristics are very helpful. This study is the first one that integrates multiple contextual factors and shows that this is feasible and beneficial on TREC collections. It paves the way for future developments in this direction. Keywords: Information retrieval. Query expansion, Query context, Term relation, Domain model, Language model