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SentiWordNet for Indian Languages
TLDR
Multiple computational techniques like, WordNet based, dictionary based, Dictionary based, corpus based or generative approaches for generating SentiWordNet(s) for Indian languages are proposed.
Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining
TLDR
The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label by way of concept-based opinion mining.
Language Independent Named Entity Recognition in Indian Languages
This paper reports about the development of a Named Entity Recognition (NER) system for South and South East Asian languages, particularly for Bengali, Hindi, Telugu, Oriya and Urdu as part of the
Named Entity Recognition using Support Vector Machine: A Language Independent Approach
TLDR
The development of a NER system for Bengali and Hindi using Support Vector Machine (SVM) and an unsupervised algorithm is developed in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus.
A Conditional Random Field Approach for Named Entity Recognition in Bengali and Hindi
TLDR
This paper describes the development of Named Entity Recognition systems for two leading Indian languages, namely Bengali and Hindi, using the Conditional Random Field (CRF) framework and considers only the tags that denote person names, location names, organization names, number expressions, time expressions and measurement expressions.
A Practical Guide to Sentiment Analysis
TLDR
The main aim of this book is to provide a feasible research platform to ambitious researchers towards developing the practical solutions that will be indeed beneficial for the authors' society, business and future researches as well.
A Modified Joint Source-Channel Model for Transliteration
TLDR
A framework has been presented that allows direct orthographical mapping between two languages that are of different origins employing different alphabet sets and a Bengali-English machine transliteration system has been developed based on the proposed models.
Bengali Named Entity Recognition Using Support Vector Machine
TLDR
It has been shown that this system outperforms other existing Bengali NER systems, and makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the various named entity (NE) classes.
Use of Machine Translation in India: Current Status
A survey of the machine translation systems that have been developed in India for translation from English to Indian languages and among Indian languages reveals that the MT softwares are used in
Named Entity Recognition in Bengali: A Conditional Random Field Approach
TLDR
Experimental results of the 10-fold cross validation test show the effectiveness of the proposed CRF based NER system with an overall average Recall, Precision and F-Score values of 93.8%, 87.8% and 90.7%, respectively.
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