TY - GEN
T1 - CICBUAPnlp
T2 - 9th International Workshop on Semantic Evaluation, SemEval 2015
AU - Gómez-Adorno, Helena
AU - Sidorov, Grigori
AU - Vilariño, Darnes
AU - Pinto, David
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics
PY - 2015
Y1 - 2015
N2 - This paper describes our approach for the Community Question Answering Task, which was presented at the SemEval 2015. The system should read a given question and identify good, potentially relevant, and bad answers for that question. Our approach transforms the answers of the training set into a graph based representation for each answer class, which contains lexical, morphological, and syntactic features. The answers in the test set are also transformed into the graph based representation individually. After this, different paths are traversed in the training and test sets in order to find relevant features of the graphs. As a result of this procedure, the system constructs several vectors of features: one for each traversed graph. Finally, a cosine similarity is calculated between the vectors in order to find the class that best matches a given answer. Our system was developed for the English language only, and it obtained an accuracy of 53.74 for subtask A and 44.0 for subtask B.
AB - This paper describes our approach for the Community Question Answering Task, which was presented at the SemEval 2015. The system should read a given question and identify good, potentially relevant, and bad answers for that question. Our approach transforms the answers of the training set into a graph based representation for each answer class, which contains lexical, morphological, and syntactic features. The answers in the test set are also transformed into the graph based representation individually. After this, different paths are traversed in the training and test sets in order to find relevant features of the graphs. As a result of this procedure, the system constructs several vectors of features: one for each traversed graph. Finally, a cosine similarity is calculated between the vectors in order to find the class that best matches a given answer. Our system was developed for the English language only, and it obtained an accuracy of 53.74 for subtask A and 44.0 for subtask B.
UR - http://www.scopus.com/inward/record.url?scp=85084105589&partnerID=8YFLogxK
M3 - Contribución a la conferencia
AN - SCOPUS:85084105589
T3 - SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings
SP - 18
EP - 22
BT - SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics
A2 - Nakov, Preslav
A2 - Zesch, Torsten
A2 - Cer, Daniel
A2 - Jurgens, David
PB - Association for Computational Linguistics (ACL)
Y2 - 4 June 2015 through 5 June 2015
ER -