A new approach for named entity recognition
Künye
Ertopçu, B., Kanburoğlu, A. B., Topsakal, O., Açıkgöz, O., Gürkan, A. T., Özenç, B., Avar, B., Ercan, G., Çam, İ. & Yıldız, O. T. (2017). A new approach for named entity recognition. Paper presented at the 2nd International Conference on Computer Science and Engineering, UBMK 2017, 474-479. doi:10.1109/UBMK.2017.8093439Özet
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.
