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The CHEMDNER corpus of chemicals and drugs and its annotation principles
The CHEMDNER corpus is presented, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. Expand
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis
A recurrent neural network based multi-modal attention framework that leverages the contextual information for utterance-level sentiment prediction that applies attention on multi- modal multi-utterance representations and tries to learn the contributing features amongst them. Expand
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 theExpand
Named Entity Recognition using Support Vector Machine: A Language Independent Approach
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. Expand
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
A novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks and it is shown that this method outperforms the state of the art by 1.6% on average in two distinct domains: restaurant and laptop. Expand
Bengali Named Entity Recognition Using Support Vector Machine
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. Expand
A Conditional Random Field Approach for Named Entity Recognition in Bengali and Hindi
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. Expand
A Modified Joint Source-Channel Model for Transliteration
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. Expand
Weighted Vote-Based Classifier Ensemble for Named Entity Recognition: A Genetic Algorithm-Based Approach
Results show that the vote based classifier ensemble identified by the GA-based approach outperforms all the individual classifiers, three conventional baseline ensembles, and some other existing ensemble techniques. Expand
Named Entity Recognition in Bengali: A Conditional Random Field Approach
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. Expand