基于BiLSTM模型的英语语料库标注研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI:10.1109/acait53529.2021.9731116

Juanjuan Guo

{"title":"基于BiLSTM模型的英语语料库标注研究","authors":"Juanjuan Guo","doi":"10.1109/acait53529.2021.9731116","DOIUrl":null,"url":null,"abstract":"The recognition and tagging of special words in English corpus can effectively improve students' learning efficiency. Based on BiLSTM model and CRF model, a BiLSTM-CRF model model is constructed to recognize and automatically label special words in English corpus. The results show that the average accuracy of BiLSTM-CRF model is 95.35% and the average recall rate is 94.83%, which are much higher than other models. We can know from the above that BiLSTM-CRF model can label English professional corpora well and is a practical method.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on English Corpus Tagging Based on BiLSTM model\",\"authors\":\"Juanjuan Guo\",\"doi\":\"10.1109/acait53529.2021.9731116\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The recognition and tagging of special words in English corpus can effectively improve students' learning efficiency. Based on BiLSTM model and CRF model, a BiLSTM-CRF model model is constructed to recognize and automatically label special words in English corpus. The results show that the average accuracy of BiLSTM-CRF model is 95.35% and the average recall rate is 94.83%, which are much higher than other models. We can know from the above that BiLSTM-CRF model can label English professional corpora well and is a practical method.\",\"PeriodicalId\":173633,\"journal\":{\"name\":\"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/acait53529.2021.9731116\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/acait53529.2021.9731116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

对英语语料库中的特殊词汇进行识别和标注，可以有效地提高学生的学习效率。在BiLSTM模型和CRF模型的基础上，构建了一个BiLSTM-CRF模型模型，用于识别和自动标注英语语料库中的特殊词。结果表明，BiLSTM-CRF模型的平均准确率为95.35%，平均召回率为94.83%，大大高于其他模型。从上面可以看出，BiLSTM-CRF模型可以很好地标注英语专业语料库，是一种实用的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Research on English Corpus Tagging Based on BiLSTM model

The recognition and tagging of special words in English corpus can effectively improve students' learning efficiency. Based on BiLSTM model and CRF model, a BiLSTM-CRF model model is constructed to recognize and automatically label special words in English corpus. The results show that the average accuracy of BiLSTM-CRF model is 95.35% and the average recall rate is 94.83%, which are much higher than other models. We can know from the above that BiLSTM-CRF model can label English professional corpora well and is a practical method.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)

自引率

0.00%

发文量