Exploratory Analysis of Highly Heterogeneous Document Collections
This work addresses the challenge for users needing to locate specific documents (e.g., military critical technologies) in large, heterogeneous information collections, representing a domain-specific incremental improvement.
The researchers tackled the problem of exploratory analysis of highly heterogeneous document collections by developing a multifaceted system that uses automated tagging and faceted browsing, introducing the KERA algorithm for unsupervised keyword extraction which was significantly more effective than state-of-the-art methods.
We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents in a purely automated fashion and exploiting these tags in a powerful faceted browsing framework. Tagging strategies employed include both unsupervised and supervised approaches based on machine learning and natural language processing. As one of our key tagging strategies, we introduce the KERA algorithm (Keyword Extraction for Reports and Articles). KERA extracts topic-representative terms from individual documents in a purely unsupervised fashion and is revealed to be significantly more effective than state-of-the-art methods. Finally, we evaluate our system in its ability to help users locate documents pertaining to military critical technologies buried deep in a large heterogeneous sea of information.