{"citation":{"chicago":"Kim, Yeongsu , Seungwoo Lee, Markus Dollmann, and Michaela Geierhos. “Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure.” <i>International Journal of Advanced Science and Technology</i> 112 (2018): 123–36. <a href=\"https://doi.org/10.14257/ijast.2018.112.12\">https://doi.org/10.14257/ijast.2018.112.12</a>.","apa":"Kim, Y., Lee, S., Dollmann, M., &#38; Geierhos, M. (2018). Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure. <i>International Journal of Advanced Science and Technology</i>, <i>112</i>, 123–136. <a href=\"https://doi.org/10.14257/ijast.2018.112.12\">https://doi.org/10.14257/ijast.2018.112.12</a>","short":"Y. Kim, S. Lee, M. Dollmann, M. Geierhos, International Journal of Advanced Science and Technology 112 (2018) 123–136.","ama":"Kim Y, Lee S, Dollmann M, Geierhos M. Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure. <i>International Journal of Advanced Science and Technology</i>. 2018;112:123-136. doi:<a href=\"https://doi.org/10.14257/ijast.2018.112.12\">10.14257/ijast.2018.112.12</a>","bibtex":"@article{Kim_Lee_Dollmann_Geierhos_2018, title={Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure}, volume={112}, DOI={<a href=\"https://doi.org/10.14257/ijast.2018.112.12\">10.14257/ijast.2018.112.12</a>}, journal={International Journal of Advanced Science and Technology}, publisher={SERSC Australia}, author={Kim, Yeongsu  and Lee, Seungwoo and Dollmann, Markus and Geierhos, Michaela}, year={2018}, pages={123–136} }","mla":"Kim, Yeongsu, et al. “Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure.” <i>International Journal of Advanced Science and Technology</i>, vol. 112, SERSC Australia, 2018, pp. 123–36, doi:<a href=\"https://doi.org/10.14257/ijast.2018.112.12\">10.14257/ijast.2018.112.12</a>.","ieee":"Y. Kim, S. Lee, M. Dollmann, and M. Geierhos, “Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure,” <i>International Journal of Advanced Science and Technology</i>, vol. 112, pp. 123–136, 2018."},"_id":"2331","year":"2018","file_date_updated":"2018-11-02T15:16:29Z","ddc":["000"],"publication":"International Journal of Advanced Science and Technology","department":[{"_id":"36"},{"_id":"1"},{"_id":"579"}],"author":[{"first_name":"Yeongsu ","full_name":"Kim, Yeongsu ","last_name":"Kim"},{"last_name":"Lee","full_name":"Lee, Seungwoo","first_name":"Seungwoo"},{"first_name":"Markus","id":"27578","last_name":"Dollmann","full_name":"Dollmann, Markus"},{"orcid":"0000-0002-8180-5606","full_name":"Geierhos, Michaela","last_name":"Geierhos","first_name":"Michaela","id":"42496"}],"status":"public","language":[{"iso":"eng"}],"file":[{"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-02T15:16:29Z","file_id":"5297","date_created":"2018-11-02T15:16:29Z","file_name":"12.pdf","success":1,"access_level":"closed","file_size":586968,"creator":"ups"}],"doi":"10.14257/ijast.2018.112.12","date_created":"2018-04-13T09:19:22Z","has_accepted_license":"1","volume":112,"type":"journal_article","keyword":["Software Engineering","Natural Language Processing","Semantic Annotation","Machine Learning","Feature Engineering","Syntactic Structure"],"title":"Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure","page":"123-136","user_id":"477","publisher":"SERSC Australia","article_type":"original","abstract":[{"lang":"eng","text":"A user generally writes software requirements in ambiguous and incomplete form by using natural language; therefore, a software developer may have difficulty in clearly understanding what the meanings are. To solve this problem with automation, we propose a classifier for semantic annotation with manually pre-defined semantic categories. To improve our classifier, we carefully designed syntactic features extracted by constituency and dependency parsers. Even with a small dataset and a large number of classes, our proposed classifier records an accuracy of 0.75, which outperforms the previous model, REaCT."}],"quality_controlled":"1","publication_status":"published","date_updated":"2022-01-06T06:55:49Z","intvolume":"       112","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B1","_id":"9"}],"publication_identifier":{"issn":["2005-4238"],"eissn":["2207-6360"]}}