{"title":"汉语人称名词短语数型识别的平面化与结构化特征探讨","authors":"Jun Lang","doi":"10.1109/IALP.2011.69","DOIUrl":null,"url":null,"abstract":"Different from English, Chinese does not explicitly show grammatical number information by inflection. The Number information in a Chinese sentence is implied by the noun phrase itself and its surrounding context. In this paper, we explore diverse features, including both flat and structured, for number identification of Chinese personal noun phrase. The flat features explore the knowledge within the noun phrase while the structured features capture the surrounding context information of the noun phrase in the parse tree of the given sentence. These two kinds of features together with kernel-based SVM are utilized in this study. Evaluation on the ACE 2005 corpus shows that our method achieves 89.23% in accuracy, which significantly advances the state-of-the-art.","PeriodicalId":297167,"journal":{"name":"2011 International Conference on Asian Language Processing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Exploring Both Flat and Structured Features for Number Type Identification of Chinese Personal Noun Phrases\",\"authors\":\"Jun Lang\",\"doi\":\"10.1109/IALP.2011.69\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Different from English, Chinese does not explicitly show grammatical number information by inflection. The Number information in a Chinese sentence is implied by the noun phrase itself and its surrounding context. In this paper, we explore diverse features, including both flat and structured, for number identification of Chinese personal noun phrase. The flat features explore the knowledge within the noun phrase while the structured features capture the surrounding context information of the noun phrase in the parse tree of the given sentence. These two kinds of features together with kernel-based SVM are utilized in this study. Evaluation on the ACE 2005 corpus shows that our method achieves 89.23% in accuracy, which significantly advances the state-of-the-art.\",\"PeriodicalId\":297167,\"journal\":{\"name\":\"2011 International Conference on Asian Language Processing\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Asian Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IALP.2011.69\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2011.69","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploring Both Flat and Structured Features for Number Type Identification of Chinese Personal Noun Phrases
Different from English, Chinese does not explicitly show grammatical number information by inflection. The Number information in a Chinese sentence is implied by the noun phrase itself and its surrounding context. In this paper, we explore diverse features, including both flat and structured, for number identification of Chinese personal noun phrase. The flat features explore the knowledge within the noun phrase while the structured features capture the surrounding context information of the noun phrase in the parse tree of the given sentence. These two kinds of features together with kernel-based SVM are utilized in this study. Evaluation on the ACE 2005 corpus shows that our method achieves 89.23% in accuracy, which significantly advances the state-of-the-art.