Utpal Kumar Sikdar

Also published as: Utpal Sikdar


2018

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NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers
Biswanath Barik | Utpal Kumar Sikdar | Björn Gambäck
Proceedings of the 12th International Workshop on Semantic Evaluation

The paper presents NTNU’s contribution to SemEval-2018 Task 7 on relation identification and classification. The class weights and parameters of five alternative supervised classifiers were optimized through grid search and cross-validation. The outputs of the classifiers were combined through voting for the final prediction. A wide variety of features were explored, with the most informative identified by feature selection. The best setting achieved F1 scores of 47.4% and 66.0% in the relation classification subtasks 1.1 and 1.2. For relation identification and classification in subtask 2, it achieved F1 scores of 33.9% and 17.0%,

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Flytxt_NTNU at SemEval-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Naïve Bayes Classifiers
Utpal Kumar Sikdar | Biswanath Barik | Björn Gambäck
Proceedings of the 12th International Workshop on Semantic Evaluation

Cybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware sentences given a document (subTask1 of SemEval 2018, Task 8), as well as to classifying malware tokens in the sentences (subTask2). The approach achieved good results, ranking second of twelve participants for both subtasks, with F-scores of 57% for subTask1 and 28% for subTask2.

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Named Entity Recognition on Code-Switched Data Using Conditional Random Fields
Utpal Kumar Sikdar | Biswanath Barik | Björn Gambäck
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching

Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories. Identification of named entities in code-mixed social media texts is a more difficult and challenging task as the contexts are short, ambiguous and often noisy. This work proposes a Conditional Random Fields based named entity recognition system to identify proper names in code-switched data and classify them into nine categories. The system ranked fifth among nine participant systems and achieved a 59.25% F1-score.

2017

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Using Convolutional Neural Networks to Classify Hate-Speech
Björn Gambäck | Utpal Kumar Sikdar
Proceedings of the First Workshop on Abusive Language Online

The paper introduces a deep learning-based Twitter hate-speech text classification system. The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech. Four Convolutional Neural Network models were trained on resp. character 4-grams, word vectors based on semantic information built using word2vec, randomly generated word vectors, and word vectors combined with character n-grams. The feature set was down-sized in the networks by max-pooling, and a softmax function used to classify tweets. Tested by 10-fold cross-validation, the model based on word2vec embeddings performed best, with higher precision than recall, and a 78.3% F-score.

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A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities
Utpal Kumar Sikdar | Björn Gambäck
Proceedings of the 3rd Workshop on Noisy User-generated Text

Detecting previously unseen named entities in text is a challenging task. The paper describes how three initial classifier models were built using Conditional Random Fields (CRFs), Support Vector Machines (SVMs) and a Long Short-Term Memory (LSTM) recurrent neural network. The outputs of these three classifiers were then used as features to train another CRF classifier working as an ensemble. 5-fold cross-validation based on training and development data for the emerging and rare named entity recognition shared task showed precision, recall and F1-score of 66.87%, 46.75% and 54.97%, respectively. For surface form evaluation, the CRF ensemble-based system achieved precision, recall and F1 scores of 65.18%, 45.20% and 53.30%. When applied to unseen test data, the model reached 47.92% precision, 31.97% recall and 38.55% F1-score for entity level evaluation, with the corresponding surface form evaluation values of 44.91%, 30.47% and 36.31%.

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NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields
Erwin Marsi | Utpal Kumar Sikdar | Cristina Marco | Biswanath Barik | Rune Sætre
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We present NTNU’s systems for Task A (prediction of keyphrases) and Task B (labelling as Material, Process or Task) at SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our approach relies on supervised machine learning using Conditional Random Fields. Our system yields a micro F-score of 0.34 for Tasks A and B combined on the test data. For Task C (relation extraction), we relied on an independently developed system described in (Barik and Marsi, 2017). For the full Scenario 1 (including relations), our approach reaches a micro F-score of 0.33 (5th place). Here we describe our systems, report results and discuss errors.

2016

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Feature-Rich Twitter Named Entity Recognition and Classification
Utpal Kumar Sikdar | Björn Gambäck
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)

Twitter named entity recognition is the process of identifying proper names and classifying them into some predefined labels/categories. The paper introduces a Twitter named entity system using a supervised machine learning approach, namely Conditional Random Fields. A large set of different features was developed and the system was trained using these. The Twitter named entity task can be divided into two parts: i) Named entity extraction from tweets and ii) Twitter name classification into ten different types. For Twitter named entity recognition on unseen test data, our system obtained the second highest F1 score in the shared task: 63.22%. The system performance on the classification task was worse, with an F1 measure of 40.06% on unseen test data, which was the fourth best of the ten systems participating in the shared task.

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Language Identification in Code-Switched Text Using Conditional Random Fields and Babelnet
Utpal Kumar Sikdar | Björn Gambäck
Proceedings of the Second Workshop on Computational Approaches to Code Switching

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Twitter Named Entity Extraction and Linking Using Differential Evolution
Utpal Kumar Sikdar | Björn Gambäck
Proceedings of the 13th International Conference on Natural Language Processing

2015

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IITP: Multiobjective Differential Evolution based Twitter Named Entity Recognition
Md Shad Akhtar | Utpal Kumar Sikdar | Asif Ekbal
Proceedings of the Workshop on Noisy User-generated Text

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IITP: Hybrid Approach for Text Normalization in Twitter
Md Shad Akhtar | Utpal Kumar Sikdar | Asif Ekbal
Proceedings of the Workshop on Noisy User-generated Text

2014

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IITP: A Supervised Approach for Disorder Mention Detection and Disambiguation
Utpal Kumar Sikdar | Asif Ekbal | Sriparna Saha
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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Adapting a State-of-the-art Anaphora Resolution System for Resource-poor Language
Utpal Sikdar | Asif Ekbal | Sriparna Saha | Olga Uryupina | Massimo Poesio
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Differential Evolution Based Feature Selection and Classifier Ensemble for Named Entity Recognition
Utpal Kumar Sikdar | Asif Ekbal | Sriparna Saha
Proceedings of COLING 2012