2014
Presentations
Adrian Chifu
Difficult query predictors: combining statistical and semantic measures Presentation
Seminary, 07.02.2014.
BibTeX | Tags:
@misc{Chifu2Pres2014,
title = {Difficult query predictors: combining statistical and semantic measures},
author = {Adrian Chifu},
year = {2014},
date = {2014-02-07},
urldate = {2014-02-07},
howpublished = {Seminary},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Adrian Chifu
Expansion sélective de requêtes par apprentissage Presentation
Seminary, 03.02.2014.
BibTeX | Tags:
@misc{ChifuPres2014,
title = {Expansion sélective de requêtes par apprentissage},
author = {Adrian Chifu},
year = {2014},
date = {2014-02-03},
urldate = {2014-02-03},
howpublished = {Seminary},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
2013
Conferences
Adrian-Gabriel Chifu
Prédire la Difficulté des Requêtes: la Combinaison de Mesures Statistiques et Sémantiques Conference
COnférence francophone en Recherche d'Information et Applications, CORIA2013 2013.
Abstract | Links | BibTeX | Tags: Combined Predictors, Information Retrieval, Measure Correlation, Query Ambiguity, Query Difficulty, Query Performance Prediction
@conference{chifu2013predire,
title = {Prédire la Difficulté des Requêtes: la Combinaison de Mesures Statistiques et Sémantiques},
author = {Adrian-Gabriel Chifu},
url = {https://hal.archives-ouvertes.fr/hal-01145833/document},
year = {2013},
date = {2013-04-03},
urldate = {2013-01-01},
booktitle = {COnférence francophone en Recherche d'Information et Applications},
pages = {pp--191},
series = {CORIA2013},
abstract = {The performance of an Information Retrieval System (IRS) is closely related to the query. The queries that lead to retrieval failure are referenced in the literature as "difficult queries". This study aims at analysing, adapting and combining several difficulty predictors. The evaluation of the prediction is based on the correla- tion between the predicted difficulty and the IRS performance. As predictors, we have considered an ambiguity predictor, the IDF measure and a score distribution measure. We show that combining the proposed predictors, produce good results. The evaluation framework consists in the TREC7 and TREC8 ahdoc collections.},
keywords = {Combined Predictors, Information Retrieval, Measure Correlation, Query Ambiguity, Query Difficulty, Query Performance Prediction},
pubstate = {published},
tppubtype = {conference}
}
The performance of an Information Retrieval System (IRS) is closely related to the query. The queries that lead to retrieval failure are referenced in the literature as "difficult queries". This study aims at analysing, adapting and combining several difficulty predictors. The evaluation of the prediction is based on the correla- tion between the predicted difficulty and the IRS performance. As predictors, we have considered an ambiguity predictor, the IDF measure and a score distribution measure. We show that combining the proposed predictors, produce good results. The evaluation framework consists in the TREC7 and TREC8 ahdoc collections.
2012
Journal Articles
Adrian-Gabriel Chifu; Radu-Tudor Ionescu
Word Sense Disambiguation to Improve Precision for Ambiguous Queries Journal Article
In: Central European Journal of Computer Science, vol. 2, no. 4, pp. 398–411, 2012.
Abstract | Links | BibTeX | Tags: Ambiguous Queries, Difficult Queries, Document Clustering, Fusion Functions, Information Retrieval, Naïve Bayes Classification, Word Sense Disambiguation
@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}
}
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.
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