%0 Book Section %1 Othman2016 %A Othman, Nouha %A Faiz, Rim %B Natural Language Processing and Information Systems: 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22-24, 2016, Proceedings %C Cham %D 2016 %E Métais, Elisabeth %E Meziane, Farid %E Saraee, Mohamad %E Sugumaran, Vijayan %E Vadera, Sunil %I Springer International Publishing %K qa %P 127--139 %R 10.1007/978-3-319-41754-7_11 %T A Multi-lingual Approach to Improve Passage Retrieval for Automatic Question Answering %U https://doi.org/10.1007/978-3-319-41754-7_11 %X Retrieving topically relevant passages over a huge document collection is deemed to be of central importance to many information retrieval tasks, particularly to Question Answering (QA). Indeed, Passage Retrieval (PR) is a longstanding problem in QA, that has been widely studied over the last decades and still requires further efforts in order to enable a user to have a better chance to find a relevant answer to his human natural language question. This paper describes a successful attempt to improve PR and ranking for open domain QA by finding out the most relevant passage to a given question. It uses a support vector machine (SVM) model that incorporates a set of different powerful text similarity measures constituting our features. These latter include our new proposed n-gram based metric relying on the dependency degree of n-gram words of the question in the passage, as well as other lexical and semantic features which have already been proven successful in a recent Semantic Textual Similarity task (STS). We implemented a system named PRSYS to validate our approach in different languages. Our experimental evaluations have shown a comparable performance with other similar systems endowing with strong performance. %@ 978-3-319-41754-7

@inbook{Othman2016, abstract = {Retrieving topically relevant passages over a huge document collection is deemed to be of central importance to many information retrieval tasks, particularly to Question Answering (QA). Indeed, Passage Retrieval (PR) is a longstanding problem in QA, that has been widely studied over the last decades and still requires further efforts in order to enable a user to have a better chance to find a relevant answer to his human natural language question. This paper describes a successful attempt to improve PR and ranking for open domain QA by finding out the most relevant passage to a given question. It uses a support vector machine (SVM) model that incorporates a set of different powerful text similarity measures constituting our features. These latter include our new proposed n-gram based metric relying on the dependency degree of n-gram words of the question in the passage, as well as other lexical and semantic features which have already been proven successful in a recent Semantic Textual Similarity task (STS). We implemented a system named PRSYS to validate our approach in different languages. Our experimental evaluations have shown a comparable performance with other similar systems endowing with strong performance.}, added-at = {2018-01-09T18:11:20.000+0100}, address = {Cham}, author = {Othman, Nouha and Faiz, Rim}, biburl = {https://www.bibsonomy.org/bibtex/2343bb59706fd91523055cb1625ef043e/defeatnelly}, booktitle = {Natural Language Processing and Information Systems: 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22-24, 2016, Proceedings}, doi = {10.1007/978-3-319-41754-7_11}, editor = {M{\'e}tais, Elisabeth and Meziane, Farid and Saraee, Mohamad and Sugumaran, Vijayan and Vadera, Sunil}, interhash = {3e0a43aacf9af501ff52a35832b15bdc}, intrahash = {343bb59706fd91523055cb1625ef043e}, isbn = {978-3-319-41754-7}, keywords = {qa}, pages = {127--139}, publisher = {Springer International Publishing}, timestamp = {2018-01-09T18:11:20.000+0100}, title = {A Multi-lingual Approach to Improve Passage Retrieval for Automatic Question Answering}, url = {https://doi.org/10.1007/978-3-319-41754-7_11}, year = 2016 }