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
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.