{"citation":{"apa":"Dollmann, M., &#38; Geierhos, M. (2016). On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements. In <i>Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)</i> (pp. 1807–1816). Austin, TX, USA: Association for Computational Linguistics (ACL).","chicago":"Dollmann, Markus, and Michaela Geierhos. “On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements.” In <i>Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)</i>, 1807–16. Austin, TX, USA: Association for Computational Linguistics (ACL), 2016.","bibtex":"@inproceedings{Dollmann_Geierhos_2016, place={Austin, TX, USA}, title={On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements}, booktitle={Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, publisher={Association for Computational Linguistics (ACL)}, author={Dollmann, Markus and Geierhos, Michaela}, year={2016}, pages={1807–1816} }","short":"M. Dollmann, M. Geierhos, in: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics (ACL), Austin, TX, USA, 2016, pp. 1807–1816.","ieee":"M. Dollmann and M. Geierhos, “On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements,” in <i>Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)</i>, Austin, TX, USA, 2016, pp. 1807–1816.","ama":"Dollmann M, Geierhos M. On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements. In: <i>Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)</i>. Austin, TX, USA: Association for Computational Linguistics (ACL); 2016:1807-1816.","mla":"Dollmann, Markus, and Michaela Geierhos. “On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements.” <i>Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)</i>, Association for Computational Linguistics (ACL), 2016, pp. 1807–16."},"department":[{"_id":"36"},{"_id":"1"},{"_id":"579"}],"publication_status":"published","user_id":"15504","language":[{"iso":"eng"}],"year":"2016","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"9","name":"SFB 901 - Subproject B1"}],"publication":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)","has_accepted_license":"1","title":"On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements","author":[{"first_name":"Markus","last_name":"Dollmann","full_name":"Dollmann, Markus","id":"27578"},{"first_name":"Michaela","last_name":"Geierhos","full_name":"Geierhos, Michaela","orcid":"0000-0002-8180-5606","id":"42496"}],"ddc":["040"],"status":"public","date_created":"2017-10-17T12:41:26Z","page":"1807-1816","publisher":"Association for Computational Linguistics (ACL)","file_date_updated":"2018-03-21T12:33:48Z","main_file_link":[{"open_access":"1","url":"https://www.aclweb.org/anthology/D16-1186"}],"abstract":[{"text":"Users prefer natural language software requirements because of their usability and accessibility. When they describe their wishes for software development, they often provide off-topic information. We therefore present an automated approach for identifying and semantically annotating the on-topic parts of the given descriptions. It is designed to support requirement engineers in the requirement elicitation process on detecting and analyzing requirements in user-generated content. Since no lexical resources with domain-specific information about requirements are available, we created a corpus of requirements written in controlled language by instructed users and uncontrolled language by uninstructed users. We annotated these requirements regarding predicate-argument structures, conditions, priorities, motivations and semantic roles and used this information to train classifiers for information extraction purposes. The approach achieves an accuracy of 92% for the on- and off-topic classification task and an F1-measure of 72% for the semantic annotation.","lang":"eng"}],"conference":{"location":"Austin, TX, USA","end_date":"2016-11-05","start_date":"2016-11-01","name":"Conference on Empirical Methods in Natural Language Processing (EMNLP 2016)"},"quality_controlled":"1","date_updated":"2022-01-06T06:53:15Z","_id":"176","oa":"1","type":"conference","file":[{"file_id":"1535","date_created":"2018-03-21T12:33:48Z","file_size":259495,"content_type":"application/pdf","access_level":"closed","date_updated":"2018-03-21T12:33:48Z","success":1,"file_name":"176-D16-1186.pdf","relation":"main_file","creator":"florida"}],"place":"Austin, TX, USA"}