A new approach for named entity recognition
Burak Ertopçu, Ali Buğra Kanburoğlu, Ozan Topsakal, Onur Açıkgöz, Ali Tunca Gurkan, Berke Özenç, İlker Çam, Begüm Avar, Gökhan Ercan, Olcay Taner Yıldız · 2017 International Conference on Computer Science and Engineering (UBMK) · 2017
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.