Abstract

In natural language processing (NLP), named entity recognition (NER) and semantic classification are essential tasks. NER is a fundamental task, that identify named entities in text such as people, organizations, and locations. In Legal domain, NER is particularly important due to the variety of named entities that appear in legal documents and are important for legal analysis whereas Semantic classification is the process of giving each sentence in a text a semantic label, such as ”fact,””arguments,” or”judgement”. Both NER and Semantic classi- fication are critical component of many NLP applications such as Knowledge base construction and semantic search. Semantic searching of legal documents is a powerful technique that can be used to quickly find the information that you need. By combining NER and semantic classification, one can identify the key elements of a legal document and search for documents that are related to a particular topic or contain a particular type of information. This paper proposes a novel approach to NER and Semantic classification in legal domain using Long short-term memory (LSTM) model. The proposed approach is evaluated on a corpus of legal text and the result of this study suggest that LSTM models are a promising approach for NER and Semantic classification in legal domain and can be used to improve the performance of semantic searching of legal documents.