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Code mixed cross script factoid question classification - A deep learning approach
- Source :
- RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia, instname
- Publication Year :
- 2018
- Publisher :
- IOS Press, 2018.
-
Abstract
- [EN] Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions.<br />The work of the third author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.
- Subjects :
- Question classification
Statistics and Probability
Computer science
Social media content
02 engineering and technology
computer.software_genre
Code-mixing
Artificial Intelligence
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Code (cryptography)
Question answering
business.industry
Factoid
Deep learning
General Engineering
Cross-scripting
Work (electrical)
020201 artificial intelligence & image processing
Artificial intelligence
business
LENGUAJES Y SISTEMAS INFORMATICOS
computer
Natural language processing
Subjects
Details
- ISSN :
- 18758967 and 10641246
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Accession number :
- edsair.doi.dedup.....c61449153be5e6b10ac29887520670dc