Comparative Study of Indexing and Search Strategies for the Hindi, Marathi, and Bengali Languages
ACM Transactions on Asian Language Information Processing (TALIP)
Ad hoc retrieval with the Persian language
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Authorship Attribution Based on Specific Vocabulary
ACM Transactions on Information Systems (TOIS)
Translation techniques in cross-language information retrieval
ACM Computing Surveys (CSUR)
Toward a model of domain-specific search
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
| Hi-index | 0.00 |
It is important in information retrieval (IR), information extraction, or classification tasks that morphologically related forms are conflated under the same stem (using stemmer) or lemma (using morphological analyzer). To achieve this for the English language, algorithmic stemming or various morphological analysis approaches have been suggested. Based on Cross-Language Evaluation Forum test collections containing 284 queries and various IR models, this article evaluates these word-normalization proposals. Stemming improves the mean average precision significantly by around 7% while performance differences are not significant when comparing various algorithmic stemmers or algorithmic stemmers and morphological analysis. Accounting for thesaurus class numbers during indexing does not modify overall retrieval performances. Finally, we demonstrate that including a stop word list, even one containing only around 10 terms, might significantly improve retrieval performance, depending on the IR model. © 2009 Wiley Periodicals, Inc.