Academic Publications

@article{chifu2015word,

title = {Word sense discrimination in information retrieval: A spectral clustering-based approach},

author = {Adrian-Gabriel Chifu and Florentina Hristea and Josiane Mothe and Marius Popescu},

url = {https://hal.archives-ouvertes.fr/hal-01153775/document},

year  = {2015},

date = {2015-03-01},

urldate = {2015-01-01},

journal = {Information Processing & Management},

volume = {51},

number = {2},

pages = {16--31},

publisher = {Elsevier},

abstract = {Word sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones. In this paper we propose a new unsupervised method that uses word sense discrimination in IR. The method we develop is based on spectral clustering and reorders an initially retrieved doc- ument list by boosting documents that are semantically similar to the target query. For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms. We are interested in improving the level of precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30) respectively. We show that precision can be improved by 8% above current state-of-the-art baselines. We also focus on poor performing queries.},

keywords = {High Precision, Information Retrieval, Spectral Clustering, Word Sense Disambiguation, Word Sense Discrimination},

pubstate = {published},

tppubtype = {article}

}

@article{chifu2012word,

title = {Word Sense Disambiguation to Improve Precision for Ambiguous Queries},

author = {Adrian-Gabriel Chifu and Radu-Tudor Ionescu},

url = {https://www.researchgate.net/publication/257910091_Word_sense_disambiguation_to_improve_precision_for_ambiguous_queries},

year  = {2012},

date = {2012-12-28},

urldate = {2012-01-01},

journal = {Central European Journal of Computer Science},

volume = {2},

number = {4},

pages = {398--411},

publisher = {Springer},

abstract = {Success in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.},

keywords = {Ambiguous Queries, Difficult Queries, Document Clustering, Fusion Functions, Information Retrieval, Naïve Bayes Classification, Word Sense Disambiguation},

pubstate = {published},

tppubtype = {article}

}