{"title":"基于全局线性模型的孟加拉语词性标注","authors":"Sankar Mukherjee, Shyamal Kumar Das Mandal","doi":"10.1109/INDCON.2013.6726132","DOIUrl":null,"url":null,"abstract":"The paper describes an automatic parts-of-speech tagging for Bengali sentences using Global Linear Model (GLM) which learns to represent the whole sentence through a feature vector called Global feature. Tagger has been trained using averaged perceptron algorithm. Performance of this tagger has been compared to Conditional Random Field (CRF), Support Vector Machine (SVM), Hidden Markov Model (HMM) and Maximum Entropy (ME) based Bengali POS tagger. Experimental results show that GLM based Bengali POS tagger has the accuracy of 93.12 %.","PeriodicalId":313185,"journal":{"name":"2013 Annual IEEE India Conference (INDICON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Bengali parts-of-speech tagging using Global Linear Model\",\"authors\":\"Sankar Mukherjee, Shyamal Kumar Das Mandal\",\"doi\":\"10.1109/INDCON.2013.6726132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes an automatic parts-of-speech tagging for Bengali sentences using Global Linear Model (GLM) which learns to represent the whole sentence through a feature vector called Global feature. Tagger has been trained using averaged perceptron algorithm. Performance of this tagger has been compared to Conditional Random Field (CRF), Support Vector Machine (SVM), Hidden Markov Model (HMM) and Maximum Entropy (ME) based Bengali POS tagger. Experimental results show that GLM based Bengali POS tagger has the accuracy of 93.12 %.\",\"PeriodicalId\":313185,\"journal\":{\"name\":\"2013 Annual IEEE India Conference (INDICON)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Annual IEEE India Conference (INDICON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDCON.2013.6726132\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Annual IEEE India Conference (INDICON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDCON.2013.6726132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
摘要
本文描述了一种基于全局线性模型(Global Linear Model, GLM)的孟加拉语句子词性自动标注方法，该方法通过全局特征向量来学习表示整个句子。使用平均感知器算法训练Tagger。将该标注器的性能与条件随机场(CRF)、支持向量机(SVM)、隐马尔可夫模型(HMM)和基于最大熵(ME)的孟加拉语POS标注器进行了比较。实验结果表明，基于GLM的孟加拉语词性标注准确率为93.12%。
Bengali parts-of-speech tagging using Global Linear Model
The paper describes an automatic parts-of-speech tagging for Bengali sentences using Global Linear Model (GLM) which learns to represent the whole sentence through a feature vector called Global feature. Tagger has been trained using averaged perceptron algorithm. Performance of this tagger has been compared to Conditional Random Field (CRF), Support Vector Machine (SVM), Hidden Markov Model (HMM) and Maximum Entropy (ME) based Bengali POS tagger. Experimental results show that GLM based Bengali POS tagger has the accuracy of 93.12 %.
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