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Found 30 papers, 0 papers with code
SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!
The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes.
Studying Generalisability across Abusive Language Detection Datasets
Work on Abusive Language Detection has tackled a wide range of subtasks and domains.
A Platform Agnostic Dual-Strand Hate Speech Detector
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.
NIT\_Agartala\_NLP\_Team at SemEval-2019 Task 6: An Ensemble Approach to Identifying and Categorizing Offensive Language in Twitter Social Media Corpora
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{'}.
The Effects of User Features on Twitter Hate Speech Detection
The paper investigates the potential effects user features have on hate speech classification.
Named Entity Recognition on Code-Switched Data Using Conditional Random Fields
Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories.
Flytxt\_NTNU at SemEval-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Na\"\ive Bayes Classifiers
Cybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge.
NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers
For relation identification and classification in subtask 2, it achieved F1 scores of 33. 9{\%} and 17. 0{\%},
A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities
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{\%}.
Using Convolutional Neural Networks to Classify Hate-Speech
The paper introduces a deep learning-based Twitter hate-speech text classification system.
A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content
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.
Feature-Rich Twitter Named Entity Recognition and Classification
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.
Comparing the Level of Code-Switching in Corpora
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.
