TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers

no code implementations • SEMEVAL 2017 • Mohammed R. H. Qwaider, Abed Alhakim Freihat, Fausto Giunchiglia

In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al., 2017). We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy. Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis. Our system obtained a comparable resultsto Machine learning systems.

BIG-bench Machine Learning Named Entity Recognition (NER) +1

TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks

no code implementations • COLING 2016 • Bernardo Magnini, Anne-Lyse Minard, Mohammed R. H. Qwaider, Manuela Speranza

This paper presents TextPro-AL (Active Learning for Text Processing), a platform where human annotators can efficiently work to produce high quality training data for new domains and new languages exploiting Active Learning methodologies.

Active Learning BIG-bench Machine Learning +1

TranscRater: a Tool for Automatic Speech Recognition Quality Estimation

no code implementations • ACL 2016 • Shahab Jalalvand, Matteo Negri, Marco Turchi, José G. C. de Souza, Falavigna Daniele, Mohammed R. H. Qwaider

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3