Complexity Metric for Code-Mixed Social Media Text

TL;DR

This paper introduces a new complexity metric for code-mixed social media texts that better captures the multilingual intricacies and can be applied at various textual levels, improving upon existing metrics.

Contribution

The paper proposes an improved complexity index for code-mixed texts, addressing limitations of existing metrics and enabling analysis at sentence, paragraph, and document levels.

Findings

The new index effectively reflects the variety and complexity of multilingual documents.

It can be applied seamlessly to sentences, paragraphs, and entire documents.

The metric outperforms existing measures in capturing code-mixing complexity.

Abstract

An evaluation metric is an absolute necessity for measuring the performance of any system and complexity of any data. In this paper, we have discussed how to determine the level of complexity of code-mixed social media texts that are growing rapidly due to multilingual interference. In general, texts written in multiple languages are often hard to comprehend and analyze. At the same time, in order to meet the demands of analysis, it is also necessary to determine the complexity of a particular document or a text segment. Thus, in the present paper, we have discussed the existing metrics for determining the code-mixing complexity of a corpus, their advantages, and shortcomings as well as proposed several improvements on the existing metrics. The new index better reflects the variety and complexity of a multilingual document. Also, the index can be applied to a sentence and seamlessly extended to a paragraph or an entire document. We have employed two existing code-mixed corpora to suit the requirements of our study.