Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data

Abstract

Named Entity Recognition(NER) is one of the important tasks in Natural Language Processing(NLP) and also is a sub task of Information Extraction. In this paper we present our work on NER in Telugu-English code-mixed social media data. Code-Mixing, a progeny of multilingualism is a way in which multilingual people express themselves on social media by using linguistics units from different languages within a sentence or speech context. Entity Extraction from social media data such as tweets(twitter) 1 is in general difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. We present a Telugu-English code-mixed corpus with the corresponding named entity tags. The named entities used to tag data are Person('Per'), Organization('Org') and Location('Loc'). We experimented with the machine learning models Conditional Random Fields(CRFs), Decision Trees and Bidirectional LSTMs on our corpus which resulted in a F1-score of 0.96, 0.94 and 0.95 respectively.

Towards Neural Named Entity Recognition System in Tigrinya with Large-scale Dataset

“…The annotation of the Named Entities and Parts of Speech was performed by four native speakers of Tigrinya. To evaluate the quality of the task, we conducted Interannotator agreement (IAA) tests computed by Fleiss' kappa to measure the harmony in our decisions Srirangam et al (2019). Fleiss' kappa is the better choice than Cohen's kappa in this situation as the number of annotators is greater than two.…”