Automatic Morphological Query Expansion Using Analogy-Based Machine Learning

Information retrieval systems (IRSs) usually suffer from a low ability to recognize a same idea that is expressed in different forms. A way of improving these systems is to take into account morphological variants. We propose here a simple yet effective method to recognize these variants that are further used so as to enrich queries. In comparison with already published methods, our system does not need any external resources or a priori knowledge and thus supports many languages. This new approach is evaluated against several collections, 6 different languages and is compared to existing tools such as a stemmer and a lemmatizer. Reported results show a significant and systematic improvement of the whole IRS efficiency both in terms of precision and recall for every language. Key words: Morphological variation, query expansion, analogy-based machine learning, unsupervised machine learning

• Type: Conference
• Year: 2007
• Where: ECIR
• Authors: Fabienne Moreau, Vincent Claveau, Pascale Sébillot
• Keywords: Account Morphological Variants, ECIR 2007, Information Retrieval Systems, Information Technology, Machine Learning