Conditional Random Fields based Named Entity Recognition for Sinhala

Abstract

Named Entity Recognition (NER) plays an important role in Natural Language Processing (NLP). Named Entities (NEs) are special atomic elements in natural languages belonging to predefined categories such as persons, organizations, locations, expressions of times, quantities, monetary values and percentages etc. These are referring to specific things and not listed in grammar or lexicons. NER is the task of identifying such NEs. This is a task entwined with number of challenges. Entities may be difficult to find at first, and once found, difficult to classify. For instance, locations and person names can be the same, and follow similar formatting. This becomes tough when it comes to South and South East Asian languages. That is mainly due to the nature of these languages. Even though Latin languages have accurate NER solutions those cannot be directly applied for Indic languages, because the features found in those languages are different from English. Therefore the research was based on producing a mathematical model which acts as the integral part of the Sinhala NER system. The researchers used Sinhala News corpus as the data set to train the Conditional Random Fields (CRFs) algorithm. 90% of the corpus was used in training the model, 10% is used in testing the resulted model. The research makes use of orthographic word-level features along with contextual information, which are helpful in predicting three different NE classes namely Persons, Locations and Organizations. The findings of the research were applied in developing the NE Annotator which identified NE classes from unstructured Sinhala text. The prominent contribution of this research for NER could benefit Sinhala NLP application developers and NLP related researchers in near future.