{"title":"高引用文本的变音符化:以一本古典阿拉伯书为例","authors":"A. Alosaimy, E. Atwell","doi":"10.1109/ASAR.2018.8480176","DOIUrl":null,"url":null,"abstract":"We present a robust and accurate diacritization method of highly cited texts by automatically \"borrowing\" diacritization from similar contexts. This method of diacritization has been tested on diacritizing one book: \"Riyad As-Salheen\", for the purpose of morphological annotation of the Sunnah Arabic Corpus. The original source of Riyad is about 48.66% diacritized, and after borrowing diacritization, the percentage jumps to 76.41% with low diacritic error rate (0.004), compared to 61.73% (DER=0.214) using MADAMIRA toolkit, and 67.68% (DER=0.006) using Farasa toolkit. More importantly, this method has reduced the word ambiguity from 4.83 diacritized form/word to 1.91.","PeriodicalId":165564,"journal":{"name":"2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Diacritization of a Highly Cited Text: A Classical Arabic Book as a Case\",\"authors\":\"A. Alosaimy, E. Atwell\",\"doi\":\"10.1109/ASAR.2018.8480176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a robust and accurate diacritization method of highly cited texts by automatically \\\"borrowing\\\" diacritization from similar contexts. This method of diacritization has been tested on diacritizing one book: \\\"Riyad As-Salheen\\\", for the purpose of morphological annotation of the Sunnah Arabic Corpus. The original source of Riyad is about 48.66% diacritized, and after borrowing diacritization, the percentage jumps to 76.41% with low diacritic error rate (0.004), compared to 61.73% (DER=0.214) using MADAMIRA toolkit, and 67.68% (DER=0.006) using Farasa toolkit. More importantly, this method has reduced the word ambiguity from 4.83 diacritized form/word to 1.91.\",\"PeriodicalId\":165564,\"journal\":{\"name\":\"2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASAR.2018.8480176\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASAR.2018.8480176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Diacritization of a Highly Cited Text: A Classical Arabic Book as a Case
We present a robust and accurate diacritization method of highly cited texts by automatically "borrowing" diacritization from similar contexts. This method of diacritization has been tested on diacritizing one book: "Riyad As-Salheen", for the purpose of morphological annotation of the Sunnah Arabic Corpus. The original source of Riyad is about 48.66% diacritized, and after borrowing diacritization, the percentage jumps to 76.41% with low diacritic error rate (0.004), compared to 61.73% (DER=0.214) using MADAMIRA toolkit, and 67.68% (DER=0.006) using Farasa toolkit. More importantly, this method has reduced the word ambiguity from 4.83 diacritized form/word to 1.91.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
