Creation of Corpus and Analysis in Code-Mixed K annada- E nglish Social Media Data for POS Tagging (2020)

Abstract: Part-of-Speech (POS) is one of the essential tasks for many Natural Language Processing (NLP) applications. There has been a significant amount of work done in POS tagging for resource-rich languages. POS tagging is an essential phase of text analysis in understanding the semantics and context of language. These tags are useful for higher-level tasks such as building parse trees, which can be used for Named Entity Recognition, Coreference resolution, Sentiment Analysis, and Question Answering. There has been work done on code-mixed social media corpus but not on POS tagging of Kannada-English code-mixed data. Here, we present Kannada-English code- mixed social media corpus annotated with corresponding POS tags. We also experimented with machine learning classification models CRF, Bi-LSTM, and Bi-LSTM-CRF models on our corpus.

## introduction the advent of social media like twitter, facebook, and reddit has accelerated the communication between people of all colors, nations, and languages. though the platform exists, the barriers to communication still exist due to languages. many researchers are trying to solve this through various methods. india is a land of multiple languages and majority of people are multilingual and tend to mix words from different languages in written text and also in speech. this interchanging language method involves complex grammar and is commonly addressed by terms 'code-mixing' and 'code-switching' as described by @xcite . code-switching refers to the use of words or phrases from different languages within the same speech context, whereas code-mixing refers to the use of words or phrases from different languages in the same sentence. we can understand the difference between code-mixing and code-switching from the positions of altered elements. codemixing refers to the intra-sentential modification of codes, whereas code-switching refers to the intersentential modification of codes. ## corpus creation and annotation this corpus consists of kannada-english code mixed tweets scraped from twitter for the past six years based on topics such as sports, trending hashtags, politics, movies, events, and others not limited to a particular domain. the tweets were collected using twintproject foot_0 -an opensource twitter intelligence tool. we retrieved over 318,000 tweets using the mentioned tool. after extensive cleaning and pre-processing of tweets, we were left with 6468 code-mixed kannada-english tweets. we have done extensive pre-processing of tweets and retrieved them in json format. this json formatted data includes metadata like urls, usernames, retweets, tweet ids, likes, full names, and others. the following steps were followed during preprocessing : 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 318,000 of tweets from twitter using twintproject. after extensive cleaning, we were left with 6468 code-mixed kannada-english tweets, as part of annotation using sixteen pos tags along with 'x' tag for foreign words, we tagged 156761 tokens (refer ## experiments we present the experiments using a combination of features and systems. to understand the effect of different parameters and features of the model, we performed several experiments. with some set of features at once and all at a time simultaneously, we performed experiments while 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 three-fold crossvalidation for crf. we used 'scikit-learn,' 'tensorflow,' and 'keras' libraries to implement the above algorithms. ## results and discussion in both the experiments bi-lstm and bi-lstm-crf, we experimented with the optimizer, activation functions, and the number of epochs. after several experiments, the best result we came through was using 'softmax' as activation function, 'rmsprop' as an optimizer and 'categorical cross-entropy' as our loss function. table2 shows the results of bilstm on our corpus using thirty epochs, and also shows the results of bi-lstm-crf on our corpus using twenty epochs, both with random initialization of embedding vectors. the training, validation, and testing for both experiments are 60%, 10%, and 30% of the total data, respectively. bi-lstm resulted in best f1-score of 0.80 and bi-lstm-crf with best f1-score of 0.81. ## conclusion and future work our contributions are as follows: for future work, the corpus can also be enriched by giving the ner tags for each token. the size of the corpus can be increased with more data. the problem can be adapted for pos tagging in multilingual code-mixed data.