Mixed graph of terms for query expansion

• Published in: 2011 11th International Conference on Intelligent Systems Design and Applications pp. 581 - 586
• Main Authors: Clarizia, F., Colace, F., De Santo, M., Greco, L., Napoletano, P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2011
• Subjects: Erbium; Feature extraction; Generators; Intelligent systems; Probabilistic logic; probabilistic topic model; query expansion; relevance feedback; Semantics; term extraction; Text retrieval; Vectors
• ISSN: 2164-7143; 2164-7151
• DOI: 10.1109/ISDA.2011.6121718

It is well known that one way to improve the accuracy of a text retrieval system is to expand the original query with additional knowledge coded through topic-related terms. In the case of an interactive environment, the expansion, which is usually represented as a list of words, is extracted from documents whose relevance is known thanks to the feedback of the user. In this paper we argue that the accuracy of a text retrieval system can be improved if we employ a query expansion method based on a mixed Graph of Terms representation instead of a method based on a simple list of words. The graph, that is composed of a directed and an undirected subgraph, can be automatically extracted from a small set of only relevant documents (namely the user feedback) using a method for term extraction based on the probabilistic Topic Model. The evaluation of the proposed method has been carried out by performing a comparison with two less complex structures: one represented as a set of pairs of words and another that is a simple list of words.