Named Entity Recognition for Code-Mixed K annada- E nglish Social Media Data (2022)

Abstract: Named Entity Recognition (NER) is a critical task in the field of Natural Language Processing (NLP) and is also a sub-task of Information Extraction. There has been a significant amount of work done in entity extraction and Named Entity Recognition for resource-rich languages. Entity extraction from code-mixed social media data like tweets from twitter complicates the problem due to its unstructured, informal, and incomplete information available in tweets. Here, we present work on NER in Kannada-English code-mixed social media corpus with corresponding named entity tags referring to Organisation (Org), Person (Pers), and Location (Loc). We experimented with machine learning classification models like Conditional Random Fields (CRF), Bi-LSTM, and Bi-LSTM-CRF models on our corpus.

## introduction india has twenty-three significant languages with over seven hundred and twenty dialects. kannada is one of the four major dravidian languages and it is one of the top 30 most spoken languages of the world, with its own independent script and over fifty million speakers. the majority of people are multilingual and tend to mix words from different languages in speech and written text. this method of interchanging languages involves complex grammar and is commonly addressed by terms 'code-switching' and 'code-mixing' as described by @xcite . code-mixing refers to the use of words, phrases, clauses or morphemes from different languages in the same sentence. code-switching refers to the use of words or phrases or clauses from different languages within the same speech context. we can understand the difference between codeswitching and code-mixing from the positions of altered elements. code-mixing refers to the intrasentential modification of codes, whereas code-switching refers to the inter-sentential modification of codes. ## background and related work there has been a plethora of research done on named entity recognition (ner) from the early 2000s @xcite . however, most of this is in resource-rich languages. the fire2 (forum for information retrieval and extraction) tasks have shed light on ner in indian languages. now, codemixing has found its application in various areas after fire2, such as query labeling @xcite , sentiment analysis @xcite , question classification. br and ramakanth kumar (2012) has done the work on the kannada pos tagger with probabilistic classifiers. similar work has been done by @xcite in the kannada pos tagger using machine learning models. amarappa and sathyanarayana (2013) worked on ner and classification in the kannada language. lakshmi and @xcite presented an automatic identification system for code-mixed kannada-english social media text. @xcite worked on sentiment analysis for code-mixed kannada-english social media text. to the best of our knowledge, the corpus we created is the first kannada-english code-mixed corpus with named entity tags. ## corpus and annotation this corpus consists of kannada-english code mixed tweets gathered from twitter. the tweets were collected using twintproject foot_0 -an opensource twitter intelligence tool. the tweets are from the past 6 years based on various topics such as movies, sports, celebrities, politics, trending hashtags, social events. we have retrieved a total of over 317,000 tweets using the twintproject, and after extensive cleaning and pre-processing, we were left with 6530 kannada-english code mix tweets. the pre-processing consists of the following steps. the corpus will be made available for public use as soon as possible. the following explains the mapping of the tokens with their respective tags. ## corpus statistics we have collected more than 317,000 of tweets from twitter using twintproject. after extensive cleaning, we were left with 6,530 kannada-english code mixed tweets, as part of annotation using six named entity tags along with 'other' tag we tagged 152,987 tokens. we made sure that all the words in the corpus are in roman script. we used hashtags related to politics, sports, social events and recent trends etc., in collecting the corpus. ## experiments we present all experiments using a combination of features and systems. to understand the effect of different parameters and features of the model, we performed several experiments. experiments were performed using some set of features at once and all at a time simultaneously changing the parameters of the model, like regularization parameters and algorithms of optimization like 'l2 regularization', 'average perceptron'and 'passive aggressive' for crf, optimization algorithms and loss functions in lstm. we used five-fold cross-validation for crf and three-fold for other experiments in order to validate our classification models. we used 'scikitlearn,' 'tensorflow,' and 'keras' libraries for the implementation of the above algorithms. ## conclusion and future work our contributions are as follows: lstm, and lstm-crf on our data, the f1score for which is 0.95, 0.96, and 0.96 respectively, which is looking good considering the amount of research done in this new domain. for future work, the corpus can be enriched by also giving the respective pos tags for each token. the size of the corpus can be increased with more ne tags. the problem can be adapted for ner identification in code-mixed data containing more than two languages from multilingual societies.