Language processing and learning models for community question answering in Arabic

Salvatore Romeo, Giovanni Da San Martino, Yonatan Belinkov, Alberto Barrón-Cedeño, Mohamed Eldesouki, Kareem Darwish, Hamdy Mubarak, James Glass, Alessandro Moschitti

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we focus on the problem of question ranking in community question answering (cQA) forums in Arabic. We address the task with machine learning algorithms using advanced Arabic text representations. The latter are obtained by applying tree kernels to constituency parse trees combined with textual similarities, including word embeddings. Our two main contributions are: (i) an Arabic language processing pipeline based on UIMA—from segmentation to constituency parsing—built on top of Farasa, a state-of-the-art Arabic language processing toolkit; and (ii) the application of long short-term memory neural networks to identify the best text fragments in questions to be used in our tree-kernel-based ranker. Our thorough experimentation on a recently released cQA dataset shows that the Arabic linguistic processing provided by Farasa produces strong results and that neural networks combined with tree kernels further boost the performance in terms of both efficiency and accuracy. Our approach also enables an implicit comparison between different processing pipelines as our tests on Farasa and Stanford parsers demonstrate.

Original languageEnglish
Pages (from-to)274-290
Number of pages17
JournalInformation Processing and Management
Volume56
Issue number2
DOIs
StatePublished - Mar 2017
Externally publishedYes

Keywords

  • Attention models
  • Community question answering
  • Constituency parsing in Arabic
  • Long short-term memory neural networks
  • Tree-kernel-based ranking

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences

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