Named Entity Recognition for Tamil text Using Deep Learning
Sampath Anbukkarasi, S. Varadhaganapathy, S. Jeevapriya, A. Kaaviyaa, T. Lawvanyapriya, S. Monisha · 2022 International Conference on Computer Communication and Informatics (ICCCI) · 2022
A common foundation for Natural Language Processing (NLP) applications is Named-Entity-Recognition (NER). Organizing data into pre-defined labels is a challenging task, and due to the Tamil language's unique characteristics and complexity, it becomes even more difficult. With the Multilingual Universal Sentence Encoder, we constructed a GRU model using transfer learning and deep neural networks. The enormous amount of unstructured data transmitted every day necessitates the development of effective information retrieval and extraction methods. The act of categorizing the data into pre-defined categories, known as named entity recognition, is a tough task. This work provides a novel Deep Learning technique for Standard Arabic Named Entity Recognition that outperforms previous studies. The main purpose of creating a new model is to provide more fine-grained results for Natural Language Processing applications. We compared RNN, LSTM, GRU, Bi-LSTM, and GRU using deep neural networks. (Abstract)