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Register a new userLynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets
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Added by
Kushal Chawla
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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We describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets. Our system is an ensemble of various machine learning methods, leveraging both traditional feature-based classifiers as well as recent advances in pre-trained language models that help in capturing the syntactic, semantic, and contextual features from the tweets. We further employ pseudo-labelling to incorporate the unlabelled Twitter data released on the pandemic. Our best performing model achieves an F1-score of 0.9179 on the provided validation set and 0.8805 on the blind test-set.
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The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81% on the test set.
We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Mining for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Distill- BERT on each task, as well as first fine-tuning the model on the other task. We explore how much fine-tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).
A finite mixture model is used to learn trends from the currently available data on coronavirus (COVID-19). Data on the number of confirmed COVID-19 related cases and deaths for European countries and the United States (US) are explored. A semi-supervised clustering approach with positive equivalence constraints is used to incorporate country and state information into the model. The analysis of trends in the rates of cases and deaths is carried out jointly using a mixture of multivariate Gaussian non-linear regression models with a mean trend specified using a generalized logistic function. The optimal number of clusters is chosen using the Bayesian information criterion. The resulting clusters provide insight into different mitigation strategies adopted by US states and European countries. The obtained results help identify the current relative standing of individual states and show a possible future if they continue with the chosen mitigation technique
In this paper, we describe the approach that we employed to address the task of Entity Recognition over Wet Lab Protocols -- a shared task in EMNLP WNUT-2020 Workshop. Our approach is composed of two phases. In the first phase, we experiment with various contextualised word embeddings (like Flair, BERT-based) and a BiLSTM-CRF model to arrive at the best-performing architecture. In the second phase, we create an ensemble composed of eleven BiLSTM-CRF models. The individual models are trained on random train-validation splits of the complete dataset. Here, we also experiment with different output merging schemes, including Majority Voting and Structured Learning Ensembling (SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for the partial and exact match of the entity spans, respectively. We were ranked first and second, in terms of partial and exact match, respectively.
This paper presents the results of the wet lab information extraction task at WNUT 2020. This task consisted of two sub tasks: (1) a Named Entity Recognition (NER) task with 13 participants and (2) a Relation Extraction (RE) task with 2 participants. We outline the task, data annotation process, corpus statistics, and provide a high-level overview of the participating systems for each sub task.
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