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Found 10 papers, 0 papers with code
JU_KS@SAIL_CodeMixed-2017: Sentiment Analysis for Indian Code Mixed Social Media Texts
We have also used a small SentiWordnet for English and a small SentiWordnet for Bengali.
JU_KS_Group@FIRE 2016: Consumer Health Information Search
The percentage accuracy obtained by our developed system for task1 was 73. 39 which is third highest among the 9 teams participated in the shared task.
KS_JU@DPIL-FIRE2016:Detecting Paraphrases in Indian Languages Using Multinomial Logistic Regression Model
We also participated in task1 and task2 both for all four Indian Languages.
A CRF Based POS Tagger for Code-mixed Indian Social Media Text
We participated only in constrained mode contest for all three language pairs, Bengali-English, Hindi-English and Telegu-English.
Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON 2015
In constrained mode, our system obtains average overall accuracy (averaged over all three language pairs) of 75. 60% which is very close to other participating two systems (76. 79% for IIITH and 75. 79% for AMRITA_CEN) ranked higher than our system.
A Hidden Markov Model Based System for Entity Extraction from Social Media English Text at FIRE 2015
A statistical HMM (Hidden Markov Models) based model has been used to implement our system.
An HMM Based Named Entity Recognition System for Indian Languages: The JU System at ICON 2013
This paper reports about our work in the ICON 2013 NLP TOOLS CONTEST on Named Entity Recognition.
A Machine Learning Approach for the Identification of Bengali Noun-Noun Compound Multiword Expressions
This paper presents a machine learning approach for identification of Bengali multiword expressions (MWE) which are bigram nominal compounds.
A Hybrid Approach to Extract Keyphrases from Medical Documents
Keyphrases are the phrases, consisting of one or more words, representing the important concepts in the articles.
