Mohammed R. H. Qwaider


2017

pdf bib
TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers
Mohammed R. H. Qwaider | Abed Alhakim Freihat | Fausto Giunchiglia
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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.

2016

pdf bib
TranscRater: a Tool for Automatic Speech Recognition Quality Estimation
Shahab Jalalvand | Matteo Negri | Marco Turchi | José G. C. de Souza | Daniele Falavigna | Mohammed R. H. Qwaider
Proceedings of ACL-2016 System Demonstrations

pdf bib
TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks
Bernardo Magnini | Anne-Lyse Minard | Mohammed R. H. Qwaider | Manuela Speranza
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

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. TextPro-AL is a web-based application integrating four components: a machine learning based NLP pipeline, an annotation editor for task definition and text annotations, an incremental re-training procedure based on active learning selection from a large pool of unannotated data, and a graphical visualization of the learning status of the system.