Search Results for author: Utpal Kumar Sikdar

Found 13 papers, 0 papers with code

Named Entity Recognition on Code-Switched Data Using Conditional Random Fields

no code implementations WS 2018 Utpal Kumar Sikdar, Biswanath Barik, Bj{\"o}rn Gamb{\"a}ck

Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories.

Language Identification Named Entity Recognition

A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities

no code implementations WS 2017 Utpal Kumar Sikdar, Bj{\"o}rn Gamb{\"a}ck

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{\%}.

Entity Extraction using GAN Named Entity Recognition

NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields

no code implementations SEMEVAL 2017 Erwin Marsi, Utpal Kumar Sikdar, Cristina Marco, Biswanath Barik, Rune S{\ae}tre

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).

Dependency Parsing Named Entity Recognition +1

Feature-Rich Twitter Named Entity Recognition and Classification

no code implementations WS 2016 Utpal Kumar Sikdar, Bj{\"o}rn Gamb{\"a}ck

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.

Classification Entity Extraction using GAN +4

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