Named entity boundary detection for Sinhala

Abstract

Named entity recognition (NER) can be introduced as a sequential categorizing task which contains a potential gravity in novel research arena. NER can be mentioned as the foundation for accomplishing most common natural language processing (NLP) tasks such as information extraction, information retrieval, semantic role labelling etc. Even though plenty of attempts have been employed on NE type detection, still there are plenty of avenues to be discovered under the NE boundary detection. Analyzing Sinhala related contents which have been published in social media can also be considered as one of the rising factors due to several human involvements in the recent past. The ultimate goal which is to obtain a constructive deep neural framework for determining named entity boundary detection has been achieved in a comprehensive manner and the model has been tested using Sinhala related statements which have been extracted through social media. Several objectives have been determined to accomplish this task considering the existing baselines. Several novelties have been identified to show off the uniqueness of this approach. Specifically, the novel concept “Boundary Bubbles” has been used to identify the specific entity mentions considering each head word for the identified named entities. Various experiments have been conducted based on multiple evaluation criteria and the named entity boundary detection model performs well with an average of 71% in Precision, 67% in Recall and 63% in F1 over the existing benchmarks. Hence this novel framework can be accepted as a vital solution for determining named entity boundary detection under forecasting various computational activities in social media.