A new approach for named entity recognition

Autor: Berke Ozenc, Ilker Cam, Gokhan Ercan, Onur Acikgoz, Burak Ertopcu, Ali Tunca Gurkan, Ozan Topsakal, Ali Bugra Kanburoglu, Olcay Taner Yildiz, Begüm Avar
Přispěvatelé: Işık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Işık University, Faculty of Engineering, Department of Computer Engineering, Ertopçu, Burak, Kanburoğlu, Ali Buğra, Topsakal, Ozan, Açıkgöz, Onur, Gürkan, Ali Tunca, Özenç, Berke, Ercan, Gökhan, Yıldız, Olcay Taner, Çam, İlker
Rok vydání: 2017
Předmět:
Zdroj: 2017 International Conference on Computer Science and Engineering (UBMK).
Popis: Many sentences create certain impressions on people. These impressions help the reader to have an insight about the sentence via some entities. In NLP, this process corresponds to Named Entity Recognition (NER). NLP algorithms can trace a lot of entities in the sentence like person, location, date, time or money. One of the major problems in these operations are confusions about whether the word denotes the name of a person, a location or an organisation, or whether an integer stands for a date, time or money. In this study, we design a new model for NER algorithms. We train this model in our predefined dataset and compare the results with other models. In the end we get considerable outcomes in a dataset containing 1400 sentences. This work was supported by Isik University BAP projects 14B206 and 15B201. All authors contributed equally to this work. O. A., I. C., B. E., A. T. G., A. B. K., B. O., O. T. designed and implemented discrete model experiments. They also labeled the data and wrote the manuscript. G. E. designed and implemented continuous model experiments. B. A. wrote the manuscript. O. T. Y. supervised the project, gave conceptual advice and wrote the manuscript Publisher's Version
Databáze: OpenAIRE