“Kanglish alli names!” Named Entity Recognition for K annada- E nglish Code-Mixed Social Media Data (2022)

Abstract: Code-mixing (CM) is a frequently observed phenomenon on social media platforms in multilingual societies such as India. While the increase in code-mixed content on these platforms provides good amount of data for studying various aspects of code-mixing, the lack of automated text analysis tools makes such studies difficult. To overcome the same, tools such as language identifiers and parts of-speech (POS) taggers for analysing code-mixed data have been developed. One such tool is Named Entity Recognition (NER), an important Natural Language Processing (NLP) task, which is not only a subtask of Information Extraction, but is also needed for downstream NLP tasks such as semantic role labeling. While entity extraction from social media data is generally difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. In this work, we present the first ever corpus for Kannada-English code-mixed social media data with the corresponding named entity tags for NER. We provide strong baselines with machine learning classification models such as CRF, Bi-LSTM, and Bi-LSTM-CRF on our corpus with word, character, and lexical features.

## 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 significantly. twitter alone has over 500 million test posts (tweets) per day foot_0 . india, a country with over 300 million multilingual speakers, has over 23 million users on twitter as of january 2022 foot_1 , and codeswitching can be observed heavily on this social media platform @xcite . code-switching or code-mixing foot_2 occurs when "lexical items and/or grammatical features from two languages appear in one sentence" @xcite . 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 relationship with emotional expression @xcite and identity. however, research efforts are often hindered by the lack of automated nlp tools to analyse massive amounts of code-mixed data @xcite . named entity recognition (ner) is the foundation for many tasks related to information extraction. when exploring text corpora, being able to explore and browse them by the people and places mentioned in those texts becomes an essential feature. below is an example of a code-mixed kannada-english tweet which 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). t1: saanu/person/ne next/other/en month/other/en gujarat/location/ne visit/other/en madtale/other/ka #excited/other/en :d/other/univ translation: saanu will visit gujarat next month #excited :d kannada is a dravidian language spoken majorly in the indian state of karnataka with over 56 million native and second-language (l2) speakers worldwide. kannada is also one of the six languages designated as a classical language of india by the indian government. in code-mixed kannada-english data, the mixing can happen at phrase, word, syntactic and morphological levels too @xcite . this adds to the fact that the data from twitter is already difficult to analyse given its short length, high language variation, grammatical errors, unorthodox capitalisation, and frequent use of emoticons, abbreviations and hashtags. there are widely known solutions for ner on monolingual data of high-resource languages like english @xcite and low-resource languages like kannada @xcite , but the same is not true for cm data. ner for code-mixed social media data in low-resource languages has been explored only recently (details in section 2). in this paper, we have tried to address this problem for kannada-english code-mixed social media data by creating the first ever corpus with named entity tags and providing strong baselines for the task of ner. the structure of the paper is as follows. in section 2, we review the related work. in section 3, we discuss the annotation methodology and challenges involved. in section 4, we describe the steps involved in corpus creation and data statistics. in section 5, we describe our baseline systems. in section 6, we present the results of the experiments conducted. finally, in section 7, we conclude the paper and discuss the future prospects. ## background and related work a lot of work has been done in named entity recognition (ner) for resource rich language and newswire data such as such as english @xcite , german @xcite , and spanish @xcite . however, the noisy data from social media platforms like twitter are different from traditional textual resources due to slacker grammatical structure, spelling variations, abbreviations and more @xcite . ner for monolingual tweets was explored in @xcite and @xcite . @xcite analysed facebook posts generated from hindi-english bilingual users and confirmed the presence of significant code mixing in them. @xcite addressed the problem of shallow parsing of hindi-english code-mixed social media text and developed a system for hindi-english code-mixed text that can identify the language of the words, normalise them to their standard forms, assign them their pos tag and segment into chunks. @xcite proposed a hybrid model for ner on hindi-english and tamil-english cm dataset. appidi et al. (2020) reported a work on annotating cm kannada-english data collected from twitter and creating pos tags for this corpus. @xcite presented an automatic ner of hindi-english cm data while @xcite and @xcite have presented a corpus for ner in hindi-english and telugu-english cm data respectively. for kannada-english cm data, sowmya lakshmi and @xcite have proposed an automatic word-level language identification (lid) system for sentences from social media posts. to the best of our knowledge, the corpus created for this paper is the first ever kannada-english code-mixed social media corpus with named entity tags. ## annotation methodology we label the tags with the present three named entity tags 'person', 'organisation', 'location', which using the bio standard become six ne tags. b-tag refers to beginning of a named entity and i-tag refers to the intermediate of the entity, if the name is split into multiple tokens. we use the 'other' tag for for tokens that don't lie in any of the six ne tags. the 'org' tag refers to 'organisation' entity which is the name of a socio-political organisation like 'bharatiya janatha party', 'bjp', 'jds'; institutions like 'rbi' and 'canara bank'; social media companies like 'youtube', 'twitter', 'facebook', 'whatsapp', 'google', etc. the ones which does not lie in any of the mentioned tags are assigned 'other' tag. ## experiments in this section, we present the experiments using different combinations of features and systems. in order to determine the effect of each feature and parameters of the model we performed several experiments using some set of features at once and all at a time simultaneously changing the parameters of the model, like criterion ('information gain', 'gini') and maximum depth of the tree for decision tree model, regularization parameters and algorithms of optimization like 'l2 regularization', 'avg. perceptron' and 'passive aggressive' for crf. optimization algorithms and loss functions in lstm. we used 5 fold cross validation in order to validate our classification models. we used 'scikit-learn' and 'keras' libraries in python for the implementation of the above algorithms. the training, validation, and testing for all our experiments were 60%, 10%, and 30% of the total data, respectively. ## conclusion and future work the following are our contributions in this paper. as part of future work, we plan to explore downstream tasks like semantic labelling and entityspecific sentiment analysis which makes use of ner for code-mixed data. the size of the corpus can be increased to include more data from varied topics.