{"title":"基于自组织特征映射的词法分析器","authors":"G. Menier, G. Lorette","doi":"10.1109/ICDAR.1997.620672","DOIUrl":null,"url":null,"abstract":"In a lexical analyzer, the scanning of a whole dictionary using an editing distance measure has a very high computational cost. We present a lexical analyzer designed to focus on a very limited subset of the whole dictionary. The system is based on a self-organizing feature map which maps the dictionary on to a 2D space. The neighborhood relationships on this space are then used to define a short list of hypotheses. We introduce a multi-stage pyramidal network to speed up the access, and we present the performance of the system. These results are then interpreted.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Lexical analyzer based on a self-organizing feature map\",\"authors\":\"G. Menier, G. Lorette\",\"doi\":\"10.1109/ICDAR.1997.620672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In a lexical analyzer, the scanning of a whole dictionary using an editing distance measure has a very high computational cost. We present a lexical analyzer designed to focus on a very limited subset of the whole dictionary. The system is based on a self-organizing feature map which maps the dictionary on to a 2D space. The neighborhood relationships on this space are then used to define a short list of hypotheses. We introduce a multi-stage pyramidal network to speed up the access, and we present the performance of the system. These results are then interpreted.\",\"PeriodicalId\":435320,\"journal\":{\"name\":\"Proceedings of the Fourth International Conference on Document Analysis and Recognition\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fourth International Conference on Document Analysis and Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.1997.620672\",\"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 International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.1997.620672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lexical analyzer based on a self-organizing feature map
In a lexical analyzer, the scanning of a whole dictionary using an editing distance measure has a very high computational cost. We present a lexical analyzer designed to focus on a very limited subset of the whole dictionary. The system is based on a self-organizing feature map which maps the dictionary on to a 2D space. The neighborhood relationships on this space are then used to define a short list of hypotheses. We introduce a multi-stage pyramidal network to speed up the access, and we present the performance of the system. These results are then interpreted.