UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering
In this work we describe the system built for the three English subtasks of
the SemEval 2016 Task 3 by the Department of Computer Science of the University
of Houston (UH) and the Pattern Recognition and Human Language Technology
(PRHLT) research center - Universitat Politecnica de Valencia: UH-PRHLT. Our
system represents instances by using both lexical and semantic-based similarity
measures between text pairs. Our semantic features include the use of
distributed representations of words, knowledge graphs generated with the
BabelNet multilingual semantic network, and the FrameNet lexical database.
Experimental results outperform the random and Google search engine baselines
in the three English subtasks. Our approach obtained the highest results of
subtask B compared to the other task participants.
Code (0)
등록된 구현이 없습니다.
Tasks
Community Question AnsweringKnowledge GraphsQuestion AnsweringSimilar Papers 제목 키워드 기반
PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11
PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis
This paper describes the system submitted by the PRHLT-UPV team for the task 8 of SemEval-2020: Memotion Analysis. We propose a multimodal model that combines pretrained models of the BERT and VGG architectures. The BERT…
Sentiment AnalysisPRHLT-UPV at SemEval-2020 Task 12: BERT for Multilingual Offensive Language Detection
The present paper describes the system submitted by the PRHLT-UPV team for the task 12 of SemEval-2020: OffensEval 2020. The official title of the task is Multilingual Offensive Language Identification in Social Media, a…
Language IdentificationSUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering. We achieved the best results on subtask C, and strong results on subtasks A and B, by combining a rich set of vario…
Community Question AnsweringQuestion AnsweringSChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
This paper describes SChME (Semantic Change Detection with Model Ensemble), a method usedin SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME usesa model ensemble combining signals of distri…
Change DetectionWord Embeddings