{"title":"使用BioBERT嵌入识别推文中提到的个人健康经验的神经网络","authors":"Shubham Gondane","doi":"10.18653/v1/W19-3218","DOIUrl":null,"url":null,"abstract":"This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network. We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.8456, 0.9783, 0.6818 respectively.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Neural Network to Identify Personal Health Experience Mention in Tweets Using BioBERT Embeddings\",\"authors\":\"Shubham Gondane\",\"doi\":\"10.18653/v1/W19-3218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network. We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.8456, 0.9783, 0.6818 respectively.\",\"PeriodicalId\":265570,\"journal\":{\"name\":\"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/W19-3218\",\"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 Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W19-3218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
摘要
本文描述了由ASU-NLP团队为健康应用的社交媒体挖掘(SMM4H)共享任务4开发的系统。我们从BioBERT (Lee et al.， 2019)模型中提取特征嵌入，该模型已经在训练数据集上进行了微调，并将其用作密集全连接神经网络的输入。我们在参与者系统中取得了高于平均水平的成绩，总体f1得分、准确率、精密度、召回率分别为0.8036、0.8456、0.9783、0.6818。
Neural Network to Identify Personal Health Experience Mention in Tweets Using BioBERT Embeddings
This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network. We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.8456, 0.9783, 0.6818 respectively.
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