Event Annotation and Detection in Kannada-English Code-Mixed Social Media Data

Proceedings of Recent Advances in Natural Language Processing, pages 1007–1014 Varna, Sep 4–6, 2023 https://doi.org/10.26615/978-954-452-092-2_108 1007 Event Annotation and Detection in Kannada-English Code-Mixed Social Media Data Sumukh S1, Abhinav Appidi2, Manish Shrivastava1 LTRC, IIIT-Hyderabad1, PureML2 sumukh.s@research.iiit.ac.in, appidiabhinav27@gmail.com, m.shrivastava@iiit.ac.in Abstract Code-mixing (CM) is a frequently observed phenomenon on social media platforms in mul- tilingual societies such as India. While the increase in code-mixed content on these plat- forms provides good amount of data for study- ing various aspects of code-mixing, the lack of automated text analysis tools makes such stud- ies difficult. To overcome the same, tools such as language identifiers, Parts-of-Speech (POS) taggers and Named Entity Recognition (NER) for analysing code-mixed data have been de- veloped. One such important tool is Event De- tection, an important information retrieval task which can be used to identify critical facts oc- curring in the vast streams of unstructured text data available. While event detection from text is a hard problem on its own, social media data adds to it with its informal nature, and code- mixed (Kannada-English) data further compli- cates the problem due to its word-level mixing, lack of structure and incomplete information. In this work, we have tried to address this prob- lem. We have proposed guidelines for the anno- tation of events in Kannada-English CM data and provided some baselines for the same with careful feature selection. 1 Introduction With the rising popularity of social media platforms such as Twitter, Facebook and Reddit, the volume of texts on these platforms has also grown signifi- cantly. Twitter alone has over 500 million test posts (tweets) per day1. India, a country with over 300 million multilingual speakers, has over 23 million users on Twitter as of January 20222, and code- switching can be observed heavily on this social media platform (Rijhwani et al., 2017). 1https://www.internetlivestats.com/twitter-statistics/ 2https://www.statista.com/statistics/242606/number-of- active-twitter-users-in-selected-countries/ Code-switching or code-mixing3 occurs when ”lexical items and/or grammatical features from two languages appear in one sentence”(Muysken, 2000). Multilingual society speakers often tend to switch back and forth between languages when speaking or writing, mostly in informal settings. It is of great interest to linguists because of its rela- tionship with emotional expression (Rudra et al., 2016) and identity. However, research efforts are often hindered by the lack of automated NLP tools to analyse massive amounts of code-mixed data (Rudra et al., 2016). Below is an example of a code-mixed Kannada- English tweet that has also been translated into English. Named entities have been tagged along with the language tags (Ka-Kannada, En-English, NE-Named Entity, Univ-Universal). Ka-En: Sinchu/Person/NE last/Other/En month/Other/En Ker- ala/Location/NE visit/Other/En ma- didlu/Other/Ka #beautiful/Other/En :D/Other/Univ Translation: Sinchu visited Kerala last month #beautiful :D Event detection in Natural Language Process- ing (NLP) and Information Retrieval (IR) refers to the process of identifying and extracting rele- vant information about events from text data. An event can be defined as something that happens at a particular time and place, involving one or more participants and having certain properties or attributes. The emphasis is on detecting the pres- …

Content truncated to 500 words