R. Siricharoenchai, Panchapawn Chatsuwan, Paramet Tanwanont, Sarunruk Janbradab, Navaporn Surasvadi, S. Thajchayapong
{"title":"泰语产品和行业分类代码建议系统","authors":"R. Siricharoenchai, Panchapawn Chatsuwan, Paramet Tanwanont, Sarunruk Janbradab, Navaporn Surasvadi, S. Thajchayapong","doi":"10.1109/iSAI-NLP56921.2022.9960262","DOIUrl":null,"url":null,"abstract":"In this work, a system is created to suggest product/ service code and industrial classification code for Thai language. The system can suggest UNSPSC and TSIC codes relevant to query terms via indexing search. Techniques used in this work are based on knowledge of text processing and text similarity, as well as indexing. Through a complexity analysis, the system has been proved efficient as it can retrieve data about 1,000 times faster than traditional methods. Furthermore, Mean Reciprocal Rank (MRR) was employed to evaluate the search results of 1,000 products and services. The results showed that the proposed system achieved the MRR of 0.46, indicating the relevant search result is approximately in the second or third rank. Currently, the proposed system has been implemented as a part of SMEs registration process in the OSMEP website to support Thai SMEs to access government procurement.","PeriodicalId":399019,"journal":{"name":"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Product and Industrial Classification Code Suggestion System for Thai Language\",\"authors\":\"R. Siricharoenchai, Panchapawn Chatsuwan, Paramet Tanwanont, Sarunruk Janbradab, Navaporn Surasvadi, S. Thajchayapong\",\"doi\":\"10.1109/iSAI-NLP56921.2022.9960262\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, a system is created to suggest product/ service code and industrial classification code for Thai language. The system can suggest UNSPSC and TSIC codes relevant to query terms via indexing search. Techniques used in this work are based on knowledge of text processing and text similarity, as well as indexing. Through a complexity analysis, the system has been proved efficient as it can retrieve data about 1,000 times faster than traditional methods. Furthermore, Mean Reciprocal Rank (MRR) was employed to evaluate the search results of 1,000 products and services. The results showed that the proposed system achieved the MRR of 0.46, indicating the relevant search result is approximately in the second or third rank. Currently, the proposed system has been implemented as a part of SMEs registration process in the OSMEP website to support Thai SMEs to access government procurement.\",\"PeriodicalId\":399019,\"journal\":{\"name\":\"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iSAI-NLP56921.2022.9960262\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iSAI-NLP56921.2022.9960262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Product and Industrial Classification Code Suggestion System for Thai Language
In this work, a system is created to suggest product/ service code and industrial classification code for Thai language. The system can suggest UNSPSC and TSIC codes relevant to query terms via indexing search. Techniques used in this work are based on knowledge of text processing and text similarity, as well as indexing. Through a complexity analysis, the system has been proved efficient as it can retrieve data about 1,000 times faster than traditional methods. Furthermore, Mean Reciprocal Rank (MRR) was employed to evaluate the search results of 1,000 products and services. The results showed that the proposed system achieved the MRR of 0.46, indicating the relevant search result is approximately in the second or third rank. Currently, the proposed system has been implemented as a part of SMEs registration process in the OSMEP website to support Thai SMEs to access government procurement.