Search Results for author: Bj{\"o}rn Gamb{\"a}ck

Found 30 papers, 0 papers with code

A Platform Agnostic Dual-Strand Hate Speech Detector

no code implementations WS 2019 Johannes Skjeggestad Meyer, Bj{\"o}rn Gamb{\"a}ck

Hate speech detectors must be applicable across a multitude of services and platforms, and there is hence a need for detection approaches that do not depend on any information specific to a given platform.

Word Embeddings

NIT\_Agartala\_NLP\_Team at SemEval-2019 Task 6: An Ensemble Approach to Identifying and Categorizing Offensive Language in Twitter Social Media Corpora

no code implementations SEMEVAL 2019 Steve Durairaj Swamy, Anupam Jamatia, Bj{\"o}rn Gamb{\"a}ck, Amitava Das

The paper describes the systems submitted to OffensEval (SemEval 2019, Task 6) on {`}Identifying and Categorizing Offensive Language in Social Media{'} by the {`}NIT{\_}Agartala{\_}NLP{\_}Team{'}.

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

A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content

no code implementations EACL 2017 Tushar Maheshwari, Aishwarya N. Reganti, Samiksha Gupta, Anupam Jamatia, Upendra Kumar, Bj{\"o}rn Gamb{\"a}ck, Amitava Das

Several experiments were carried out on the corpora to classify the ethical values of users, incorporating Linguistic Inquiry Word Count analysis, n-grams, topic models, psycholinguistic lexica, speech-acts, and non-linguistic information, while applying a range of machine learners (Support Vector Machines, Logistic Regression, and Random Forests) to identify the best linguistic and non-linguistic features for automatic classification of values and ethics.

General Classification Sentiment Analysis +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

Comparing the Level of Code-Switching in Corpora

no code implementations LREC 2016 Bj{\"o}rn Gamb{\"a}ck, Amitava Das

Social media texts are often fairly informal and conversational, and when produced by bilinguals tend to be written in several different languages simultaneously, in the same way as conversational speech.

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