Named Entity Recognition for Code Mixed Social Media Sentences

With the increase of internet applications and social media platforms there has been an increase in the informal way of text communication. People belonging to different regions tend to mix their regional language with English on social media text. This has been the trend with many multilingual nations now and is commonly known as code mixing. In code mixing, multiple languages are used within a statement. The problem of named entity recognition (NER) is a well-researched topic in natural language processing (NLP), but the present NER systems tend to perform inefficiently on code-mixed text. This paper proposes three approaches to improve named entity recognizers for handling code-mixing. The first approach is based on machine learning techniques such as support vector machines and other tree-based classifiers. The second approach is based on neural networks and the third approach uses long short-term memory (LSTM) architecture to solve the problem.

Automated summary

With the rise of internet applications and social media platforms, people from multilingual nations tend to mix their regional language with English on social media, forming code-mixed text. Named entity recognition in natural language processing faces challenges in handling this mixed language, and this paper proposes three approaches to improve NER for code-mixed text.