{"title":"一个基于web的智能界面,用于在问答论坛中编程内容检测","authors":"Mahdy Khayyamian, J. Kim","doi":"10.1145/2451176.2451202","DOIUrl":null,"url":null,"abstract":"In this demonstration, we introduce a novel web-based intelligent interface which automatically detects and highlights programming content (programming code and messages) in Q&A programming forums. We expect our interface helps enhancing visual presentation of such forum content and enhance effective participation.\n We solve this problem using several alternative approaches: a dictionary-based baseline method, a non-sequential Naïve Bayes classification algorithm, and Conditional Random Fields (CRF) which is a sequential labeling framework. The best results are produced by CRF method with an F1-Score of 86.9%.\n We also experimentally validate how robust our classifier is by testing the constructed CRF model built on a C++ forum against a Python and a Java dataset. The results indicate the classifier works quite well across different domains.\n To demonstrate detection results, a web-based graphical user interface is developed that accepts a user input programming forum message and processes it using trained CRF model and then displays the programming content snippets in a different font to the user.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An intelligent web-based interface for programming content detection in q&a forums\",\"authors\":\"Mahdy Khayyamian, J. Kim\",\"doi\":\"10.1145/2451176.2451202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this demonstration, we introduce a novel web-based intelligent interface which automatically detects and highlights programming content (programming code and messages) in Q&A programming forums. We expect our interface helps enhancing visual presentation of such forum content and enhance effective participation.\\n We solve this problem using several alternative approaches: a dictionary-based baseline method, a non-sequential Naïve Bayes classification algorithm, and Conditional Random Fields (CRF) which is a sequential labeling framework. The best results are produced by CRF method with an F1-Score of 86.9%.\\n We also experimentally validate how robust our classifier is by testing the constructed CRF model built on a C++ forum against a Python and a Java dataset. The results indicate the classifier works quite well across different domains.\\n To demonstrate detection results, a web-based graphical user interface is developed that accepts a user input programming forum message and processes it using trained CRF model and then displays the programming content snippets in a different font to the user.\",\"PeriodicalId\":253850,\"journal\":{\"name\":\"IUI '13 Companion\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IUI '13 Companion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2451176.2451202\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUI '13 Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2451176.2451202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An intelligent web-based interface for programming content detection in q&a forums
In this demonstration, we introduce a novel web-based intelligent interface which automatically detects and highlights programming content (programming code and messages) in Q&A programming forums. We expect our interface helps enhancing visual presentation of such forum content and enhance effective participation.
We solve this problem using several alternative approaches: a dictionary-based baseline method, a non-sequential Naïve Bayes classification algorithm, and Conditional Random Fields (CRF) which is a sequential labeling framework. The best results are produced by CRF method with an F1-Score of 86.9%.
We also experimentally validate how robust our classifier is by testing the constructed CRF model built on a C++ forum against a Python and a Java dataset. The results indicate the classifier works quite well across different domains.
To demonstrate detection results, a web-based graphical user interface is developed that accepts a user input programming forum message and processes it using trained CRF model and then displays the programming content snippets in a different font to the user.