A framework for building a natural language interface for Bangla

Mobile Computing Devices are enabling connection between people and the Internet, largest source of information in the world. However to properly utilize this knowledge in these devices most people need a Natural Language Interface. Siri, Google Now, Cortana are examples of such interfaces. Because Bangla is a low resource language building such interface is very difficult and time consuming. However due to the increasing numbers of smart phone and smart device users in the Bangla speaking regions, application developers are facing the need for such interfaces to provide web services effectively. This paper addresses this issue and gives an empirical framework on how to build a feasible Natural Language Interface in Bangla and similar low resource languages.

[1]  Sivaji Bandyopadhyay,et al.  Named entity recognition in Bengali and Hindi using support vector machine , 2011 .

[2]  Philip R. Cohen.  The role of natural language in a multimodal interface , 1992, UIST '92.

[3]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[4]  Mei-Yuh Hwang,et al.  The SPHINX-II speech recognition system: an overview , 1993, Comput. Speech Lang..

[5]  P. Mitra,et al.  Shruti-II: A vernacular speech recognition system in Bengali and an application for visually impaired community , 2010, 2010 IEEE Students Technology Symposium (TechSym).

[6]  Michael Wooldridge,et al.  Applications of intelligent agents , 1998 .

[7]  Pattie Maes,et al.  Agents that reduce work and information overload , 1994, CACM.

[8]  Sivaji Bandyopadhyay,et al.  Maximum Entropy Based Bengali Part of Speech Tagging , 2008 .

[9]  Mumit Khan,et al.  Isolated and continuous bangla speech recognition: implementation, performance and application perspective , 2007 .

[10]  Dikshant Shahi.  Apache Solr , 2015, Apress.

[11]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[12]  C. Pralet.  Changes ’ 05 International Workshop on Constraint Solving under Change and Uncertainty , 2005 .

[13]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[14]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[15]  Naushad UzZaman,et al.  Comparison of different POS Tagging Techniques (n-gram, HMM and Brill’s tagger) for Bangla , 2007 .

[16]  Arjun Das,et al.  Evaluation of Two Bengali Dependency Parsers , 2012 .

[17]  Rashedur M. Rahman,et al.  Bangla User Adaptive Word Speech Recognition: Approaches and Comparisons , 2013, Int. J. Fuzzy Syst. Appl..

[18]  Eduard H. Hovy,et al.  Learning surface text patterns for a Question Answering System , 2002, ACL.

[19]  Iryna Gurevych,et al.  WebAnno: a flexible, web-based annotation tool for CLARIN , 2014 .

[20]  Stephen F. Smith,et al.  CMRadar: A Personal Assistant Agent for Calendar Management , 2004, AAAI.

[21]  Paul Lamere,et al.  Design of the CMU Sphinx-4 Decoder , 2022 .

[22]  Adam Cheyer,et al.  The Open Agent Architecture , 1997, Autonomous Agents and Multi-Agent Systems.

[23]  Sivaji Bandyopadhyay,et al.  Bengali Named Entity Recognition Using Support Vector Machine , 2008, IJCNLP.

[24]  David Yarowsky,et al.  Language Independent Named Entity Recognition Combining Morphological and Contextual Evidence , 1999, EMNLP.