NERSE: Named Entity Recognition in Software Engineering as a Service

Abstract

Named Entity Recognition (NER) is a computational linguistics task that seek to classify every word in a document as falling into different category. NER serves as an important component for many domain specific expert systems. Software engineering is one such domain where very minimum work has been done on identifying entities specific to domain. In this paper, we present NERSE, a tool that enables the user to identify software specific entities. It is developed with machine learning algorithms trained on software specific entity categories using Conditional Random Fields (CRF) and Bidirectional Long Short-Term Memory - Conditional Random Fields (BiLSTM-CRF). NERSE identifies 22 different categories of entities specific to software engineering domain with 0.85% and 0.95% for CRF (source code for Named Entity Recognition Model CRF is available at https://github.com/prathapreddymv/NERSE ) and BiLSTM-CRF (source code for Named Entity Recognition Model BiLSTM-CRF is available at https://github.com/prathapreddymv/NERSE ) models respectively.