InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction

Hettiarachchi, Hansi; Ranasinghe, Tharindu

doi:10.18653/v1/2020.wnut-1.49

Cited by 20 publications

(15 citation statements)

References 21 publications

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“…The rest of the hyperparameters which we kept as constants are mentioned in the Appendix. When performing training, we trained five models with different random seeds and considered the majority-class self ensemble mentioned in Hettiarachchi and Ranasinghe (2020b) to get the final predictions.…”

Section: Hyperparameter Configurationsmentioning

confidence: 99%

TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation

Hettiarachchi¹,

Ranasinghe²

2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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Identifying whether a word carries the same meaning or different meaning in two contexts is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. Most of the previous work in this area rely on language-specific resources making it difficult to generalise across languages. Considering this limitation, our approach to SemEval-2021 Task 2 is based only on pretrained transformer models and does not use any language-specific processing and resources. Despite that, our best model achieves 0.90 accuracy for English-English subtask which is very compatible compared to the best result of the subtask; 0.93 accuracy. Our approach also achieves satisfactory results in other monolingual and crosslingual language pairs as well.

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Section: Hyperparameter Configurationsmentioning

confidence: 99%

TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation

Hettiarachchi¹,

Ranasinghe²

2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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“…al. [22] used transformers on COVID-19 tweets to classify informative and non-informative information. Another interesting application by Wang et.…”

Section: Related Workmentioning

confidence: 99%

Understanding the Pandemic Through Mining Covid News Using Natural Language Processing

Sadman¹,

Anjum

Gupta

et al. 2021

2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

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However, there is an insignificant number of NLP research invested in studying COVID-19. Most applications include the classification of chest X-rays and CT-scans to detect the presence of pneumonia in lungs [4], a consequence of the virus. Other research areas include studying the genome sequence of the virus [5] [6] [7] and replicating its structure to fight and find vaccines. Few NLP based research publications related to COVID-19 are sentiment classification of online tweets by Samuel et. al. [8] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodar et. al. [9].Our research dives deeper with NLP to extract crucial information from newspaper reports regarding the virus and pandemic. At this stage, we considered all genres of news that are related to the virus. This collection of newspaper reports, we termed as "NNK Dataset", is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus's awareness to the best of our knowledge. The contribution of this paper includes:

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“…Document classification can be considered as a sequence classification problem. According to recent literature, transformer architectures have shown promising results in this area (Ranasinghe et al, 2019b;Hettiarachchi and Ranasinghe, 2020).…”

Section: Subtask1: Document Classificationmentioning

confidence: 99%

“…In LM, we retrain the transformer model on the targeted dataset using the model's initial training objective before fine-tuning it for the downstream task. This step helps increase the model understanding of data (Hettiarachchi and Ranasinghe, 2020).…”

Section: Language Modelling (Lm)mentioning

confidence: 99%

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Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

2021

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This workshop is the fourth issue of a series of workshops on automatic extraction of sociopolitical events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of sociopolitical events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the stateof-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi-and cross-lingual machine learning in few-and zero-shot settings. 7

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InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction

Cited by 20 publications

References 21 publications

TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation

TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation

Understanding the Pandemic Through Mining Covid News Using Natural Language Processing

Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

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