{"title":"使用上下文价移位器增强BERT性能,用于COVID-19推文的恐慌检测","authors":"Sandra Mitrovic, Vani Kanjirangat","doi":"10.1145/3582768.3582801","DOIUrl":null,"url":null,"abstract":"Panic phenomenon is one of the main challenges in the current pandemic time. In this work, we aim to explore the approaches to detect the panic-related COVID-19 tweets. Aligned to this, we propose an unsupervised clustering approach considering negation cues as an extracted feature input to the pre-trained model. This task cannot be done by simply applying state-of-the-art transformer models, since we observed that they occasionally fail in handling negations. Hence, we propose to utilize features based on Contextual Valence Shifters (CVS) along with the pre-trained BERT embeddings. We evaluate and compare the approaches in an unsupervised setup, using standard clustering metrics on a large set of COVID-19 tweets. The obtained results show that CVS effectively facilitates negation handling (positive/negative tweet discrimination).","PeriodicalId":315721,"journal":{"name":"Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing BERT Performance with Contextual Valence Shifters for Panic Detection in COVID-19 Tweets\",\"authors\":\"Sandra Mitrovic, Vani Kanjirangat\",\"doi\":\"10.1145/3582768.3582801\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Panic phenomenon is one of the main challenges in the current pandemic time. In this work, we aim to explore the approaches to detect the panic-related COVID-19 tweets. Aligned to this, we propose an unsupervised clustering approach considering negation cues as an extracted feature input to the pre-trained model. This task cannot be done by simply applying state-of-the-art transformer models, since we observed that they occasionally fail in handling negations. Hence, we propose to utilize features based on Contextual Valence Shifters (CVS) along with the pre-trained BERT embeddings. We evaluate and compare the approaches in an unsupervised setup, using standard clustering metrics on a large set of COVID-19 tweets. The obtained results show that CVS effectively facilitates negation handling (positive/negative tweet discrimination).\",\"PeriodicalId\":315721,\"journal\":{\"name\":\"Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3582768.3582801\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3582768.3582801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhancing BERT Performance with Contextual Valence Shifters for Panic Detection in COVID-19 Tweets
Panic phenomenon is one of the main challenges in the current pandemic time. In this work, we aim to explore the approaches to detect the panic-related COVID-19 tweets. Aligned to this, we propose an unsupervised clustering approach considering negation cues as an extracted feature input to the pre-trained model. This task cannot be done by simply applying state-of-the-art transformer models, since we observed that they occasionally fail in handling negations. Hence, we propose to utilize features based on Contextual Valence Shifters (CVS) along with the pre-trained BERT embeddings. We evaluate and compare the approaches in an unsupervised setup, using standard clustering metrics on a large set of COVID-19 tweets. The obtained results show that CVS effectively facilitates negation handling (positive/negative tweet discrimination).