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Found 11 papers, 2 papers with code
Sentence Classification with Imbalanced Data for Health Applications
The recall score is also better compared to the mean score of the total submissions.
Multilingual Protest News Detection - Shared Task 1, CASE 2021
Task 1, which is the focus of this report, is on multilingual protest news detection and comprises four subtasks that are document classification (subtask 1), sentence classification (subtask 2), event sentence coreference identification (subtask 3), and event extraction (subtask 4).
Capturing Changes in Mood Over Time in Longitudinal Data Using Ensemble Methodologies
This paper presents the system description of team BLUE for Task A of the CLPsych 2022 Shared Task on identifying changes in mood and behaviour in longitudinal textual data.
Gender Bias in Transformer Models: A comprehensive survey
Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound implications for individuals' lives.
Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022
The best F1 scores achieved for Subtask 1 and 2 were 86. 19% and 54. 15%, respectively.
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News
Leveraging each of these external datasets for training, we achieved up to approximately 64% F1 on the CNC test set without additional fine-tuning.
Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018
The semantics of emoji has, to date, been considered from a static perspective.
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings
Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking.
Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling
We analyse Recurrent Neural Networks (RNNs) to understand the significance of multiple LSTM layers.
Improving Language Modelling with Noise-contrastive estimation
In this paper, we showed that NCE can be a successful approach in neural language modelling when the hyperparameters of a neural network are tuned appropriately.
