Combining the Best of Two Worlds: NLP and IR for Intranet Search

Abstract

Natural language processing (NLP) is becoming much more robust and applicable in realistic applications. One area in which NLP has still not been fully exploited is information retrieval (IR). In particular we are interested in search over intranets and other local Web sites. We see dialogue-driven search which is based on a largely automated knowledge extraction process as one of the next big steps. Instead of replying with a set of documents for a user query the system would allow the user to navigate through the extracted knowledge base by making use of a simple dialogue manager. Here we support this idea with a first task-based evaluation that we conducted on a university intranet. We automatically extracted entities like person names, organizations and locations as well as relations between entities and added visual graphs to the search results whenever a user query could be mapped into this knowledge base. We found that users are willing to interact and use those visual interfaces. We also found that users preferred such a system that guides a user through the result set over a baseline approach. The results represent an important first step towards full NLP-driven intranet search.