Authors:
Fabio Clarizia
;
Francesco Colace
;
Massimo De Santo
;
Luca Greco
and
Paolo Napoletano
Affiliation:
University of Salerno, Italy
Keyword(s):
Text retrieval, Query expansion, Term extraction, Probabilistic topic model, Relevance feedback.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Concept Mining
;
Evolutionary Computing
;
Information Extraction
;
Interactive and Online Data Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
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 wo
rds and another that is a simple list of words.
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