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Found 13 papers, 0 papers with code
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.
NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields
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).
Using Convolutional Neural Networks to Classify Hate-Speech
The paper introduces a deep learning-based Twitter hate-speech text classification system.
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{\%}.
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{\%},
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.
