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    <abstract>This paper describes our system, dubbed MoRS (Modular Ranking System), pronounced ‘Morse’, which participated in Task 3 of SemEval-2017. We used MoRS to perform the Community Question Answering Task 3, which consisted on reordering a set of comments according to their usefulness in answering the question in the thread. This was made for a large collection of questions created by a user community. As for this challenge we wanted to go back to simple, easy-to-use, and somewhat forgotten technologies that we think, in the hands of non-expert people, could be reused in their own data sets. Some of our techniques included the annotation of text, the retrieval of meta-data for each comment, POS tagging and Named Entity Recognition, among others. These gave place to syntactical analysis and semantic measurements. Finally we show and discuss our results and the context of our approach, which is part of a more comprehensive system in development, named MoQA.</abstract>
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%0 Conference Proceedings
%T MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking Tasks
%A Rodrigues, Miguel J.
%A Couto, Francisco M.
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F rodrigues-couto-2017-mors
%X This paper describes our system, dubbed MoRS (Modular Ranking System), pronounced ‘Morse’, which participated in Task 3 of SemEval-2017. We used MoRS to perform the Community Question Answering Task 3, which consisted on reordering a set of comments according to their usefulness in answering the question in the thread. This was made for a large collection of questions created by a user community. As for this challenge we wanted to go back to simple, easy-to-use, and somewhat forgotten technologies that we think, in the hands of non-expert people, could be reused in their own data sets. Some of our techniques included the annotation of text, the retrieval of meta-data for each comment, POS tagging and Named Entity Recognition, among others. These gave place to syntactical analysis and semantic measurements. Finally we show and discuss our results and the context of our approach, which is part of a more comprehensive system in development, named MoQA.
%R 10.18653/v1/S17-2046
%U https://aclanthology.org/S17-2046
%U https://doi.org/10.18653/v1/S17-2046
%P 287-291

Markdown (Informal)

[MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking Tasks](https://aclanthology.org/S17-2046) (Rodrigues & Couto, SemEval 2017)

• MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking Tasks (Rodrigues & Couto, SemEval 2017)

ACL

• Miguel J. Rodrigues and Francisco M. Couto. 2017. MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking Tasks. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 287–291, Vancouver, Canada. Association for Computational Linguistics.