{ "paper_id": "2021", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T16:21:10.197143Z" }, "title": "SSNCSE NLP@DravidianLangTech-EACL2021: Offensive Language Identification on Multilingual Code Mixing Text", "authors": [ { "first": "B", "middle": [], "last": "Bharathi", "suffix": "", "affiliation": { "laboratory": "", "institution": "Nadar College of Engineering Kalavakkam -603110", "location": {} }, "email": "bharathib@ssn.edu.in" }, { "first": "Agnusimmaculate", "middle": [], "last": "Silvia", "suffix": "", "affiliation": { "laboratory": "", "institution": "Nadar College of Engineering Kalavakkam -603110", "location": {} }, "email": "agnusimmaculate18011@cse.ssn.edu.in" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Social networks made a huge impact in almost all fields in recent years. Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. Everyday we have to deal with texts, emails or different types of messages in which there are a variety of attacks and abusive phrases. It is the moderator's decision which comments to remove from the platform because of violations and which ones to keep but an automatic software for detecting abusive languages would be useful in recent days. In this paper we describe an automatic offensive language identification from Dravidian languages with various machine learning algorithms. This is work is shared task in DravidanLangTech-EACL2021. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ((Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. This work explains the submissions made by SSNCSE NLP in DravidanLangTech-EACL2021 Code-mix tasks for Offensive language detection. We achieve F1 scores of 0.95 for Malayalam, 0.7 for Kannada and 0.73 for task2-Tamil on the test-set.", "pdf_parse": { "paper_id": "2021", "_pdf_hash": "", "abstract": [ { "text": "Social networks made a huge impact in almost all fields in recent years. Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. Everyday we have to deal with texts, emails or different types of messages in which there are a variety of attacks and abusive phrases. It is the moderator's decision which comments to remove from the platform because of violations and which ones to keep but an automatic software for detecting abusive languages would be useful in recent days. In this paper we describe an automatic offensive language identification from Dravidian languages with various machine learning algorithms. This is work is shared task in DravidanLangTech-EACL2021. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ((Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. This work explains the submissions made by SSNCSE NLP in DravidanLangTech-EACL2021 Code-mix tasks for Offensive language detection. We achieve F1 scores of 0.95 for Malayalam, 0.7 for Kannada and 0.73 for task2-Tamil on the test-set.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Social media has become an important tool to connect the people all over the world. This is because it allows its users to share the content they want quickly, efficiently and in real-time. However, usercreated content shared on social media is not always organized by the rules. In fact, nowadays, content written in an offensive language has become widespread on social media (Chakravarthi and Muralidaran, 2021; Suryawanshi and Chakravarthi, 2021) . Offensive language is defined as message which contains insulting or threatening expressions written by one person to another (Chakravarthi et al., 2020d; Mandl et al., 2020) . The severity of this problem is increasing each day; consequently, it is very important to deal with this problem in terms of government policy, social media terms and policies and online community plans (Chakravarthi et al., 2020b) . At this stage, there is a need for effective methods. Social media generates a large amount of data daily as mentioned above. Therefore, it is very difficult to manually determine offensive language on the social media even by an expert. Some words that have multiple meanings that could be offensive to some people from some places. There is an increasing demand for offensive language identification on social media texts which are largely code-mixed (Chakravarthi, 2020).", "cite_spans": [ { "start": 378, "end": 414, "text": "(Chakravarthi and Muralidaran, 2021;", "ref_id": "BIBREF8" }, { "start": 415, "end": 450, "text": "Suryawanshi and Chakravarthi, 2021)", "ref_id": "BIBREF24" }, { "start": 579, "end": 607, "text": "(Chakravarthi et al., 2020d;", "ref_id": "BIBREF11" }, { "start": 608, "end": 627, "text": "Mandl et al., 2020)", "ref_id": "BIBREF18" }, { "start": 834, "end": 862, "text": "(Chakravarthi et al., 2020b)", "ref_id": "BIBREF6" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts (Jose et al., 2020; . Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text (Chakravarthi et al., 2018 (Chakravarthi et al., , 2019 . This shared task on Offensive Language Identification in Dravidian Languages-EACL 2021 presents a new gold standard corpus for offensive language identification of codemixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English). A recorded Tamil writing has been archived for more than 2600 years Mahesan, 2019, 2020a,b) . The oldest time of Tamil writing, Sangam writing, is dated from ca. 600 BC -AD 300. It has the most established surviving writing among Dravidian languages. Over 55% of the epigraphical engravings (around 55,000) found by the Archeological Survey of India are in the Tamil language.", "cite_spans": [ { "start": 135, "end": 154, "text": "(Jose et al., 2020;", "ref_id": "BIBREF17" }, { "start": 300, "end": 326, "text": "(Chakravarthi et al., 2018", "ref_id": "BIBREF2" }, { "start": 327, "end": 355, "text": "(Chakravarthi et al., , 2019", "ref_id": null }, { "start": 685, "end": 708, "text": "Mahesan, 2019, 2020a,b)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "The remainder of the paper is organized as fol- ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "Offensive language identification for Greek language is described in (Pitenis et al., 2020) . This paper uses different machine learning and deep learning models to for offensive language identification task with Offensive Greek Tweet Dataset. Multilingual offensive language identification using cross lingual embeddings with transfer learning is carried out in (Puranik et al., 2021; Hegde et al., 2021; Yasaswini et al., 2021; Ghanghor et al., 2021b,a) . In (Razavi et al., 2010), flame detection approach which extracts features at different conceptual levels and applies multilevel classification for flame detection was described. Arabic offensive language identification task was described in (Alakrot et al., 2018) . The survey of offensive language identification task is explained in (Pradhan et al., 2020) . Machine learning approach for detecting offensive language identification on Twitter data is carried out in (Gaydhani et al., 2018) .", "cite_spans": [ { "start": 69, "end": 91, "text": "(Pitenis et al., 2020)", "ref_id": "BIBREF19" }, { "start": 363, "end": 385, "text": "(Puranik et al., 2021;", "ref_id": "BIBREF22" }, { "start": 386, "end": 405, "text": "Hegde et al., 2021;", "ref_id": "BIBREF16" }, { "start": 406, "end": 429, "text": "Yasaswini et al., 2021;", "ref_id": null }, { "start": 430, "end": 455, "text": "Ghanghor et al., 2021b,a)", "ref_id": null }, { "start": 700, "end": 722, "text": "(Alakrot et al., 2018)", "ref_id": "BIBREF0" }, { "start": 794, "end": 816, "text": "(Pradhan et al., 2020)", "ref_id": null }, { "start": 927, "end": 950, "text": "(Gaydhani et al., 2018)", "ref_id": "BIBREF12" } ], "ref_spans": [], "eq_spans": [], "section": "Related work", "sec_num": "2" }, { "text": "The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media (Chakravarthi et al., , 2020a Hande et al., 2020) . The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. The dataset containing YouTube comments with class labels such as Not-offensive, offensive-untargeted, offensive-targeted-individual, offensive-targetedgroup, offensive-targeted-other, or Not-in-indentedlanguage. The train-set, dev-set and test-set distribution with class-wise distribution is shown in Table 1 . There is a clear imbalance in the data-set distribution. This could cause a bias towards a particular class and the model trained on this data-set would be more inclined towards the dominant class.", "cite_spans": [ { "start": 218, "end": 247, "text": "(Chakravarthi et al., , 2020a", "ref_id": "BIBREF5" }, { "start": 248, "end": 267, "text": "Hande et al., 2020)", "ref_id": "BIBREF15" } ], "ref_spans": [ { "start": 815, "end": 822, "text": "Table 1", "ref_id": "TABREF1" } ], "eq_spans": [], "section": "Data-set Analysis and Preprocessing", "sec_num": "3" }, { "text": "For feature extraction, the n-gram model and BERT embedding model are experimented upon. As the content of the comments is a mix of Dravidian language grammar in Roman lexicons along with English grammar, it becomes challenging to find Table 3 : Performance of the proposed approach of Malayalam-English code mixed text using dev data pre-trained models for this context. So a simple n-gram approach is considered. Also, the advancements done by the transformer model for pretraining and the availability of multilingual trained models encourage to experiment with BERT pretrained embeddings. In the proposed approach, TFIDF, Count vectorizer and BERT embeddings were extracted from the input text. Then the extracted features were trained with different machine learning models such as K-nearest neighbour, MLP classifier, random forest classifier, Ada boost classifier, decision tree classifier and voting classifier etc. The experiments were conducted for Tamil-English, Malayalam-English, and Kannada-English data sets and best models obtained for these tasks were used to generate the scores for the testset.", "cite_spans": [], "ref_spans": [ { "start": 236, "end": 243, "text": "Table 3", "ref_id": null } ], "eq_spans": [], "section": "Experimental setup and features", "sec_num": "4" }, { "text": "The features such as TFIDF, Count vectorizer and BERT embeddings were extracted from the youtube comments specified in Tamil-English code mixed text. The extracted features were trained with different machine learning models and the models are evaluated using the development data and results are tabulated in Table 2 . From the Table 2 , count vectorizer feature with MLP classifier is giving the F1-score of 0.75.", "cite_spans": [], "ref_spans": [ { "start": 310, "end": 317, "text": "Table 2", "ref_id": "TABREF3" }, { "start": 329, "end": 336, "text": "Table 2", "ref_id": "TABREF3" } ], "eq_spans": [], "section": "Tamil-English dataset", "sec_num": "5.1" }, { "text": "The features such as TFIDF, Count vectorizer and BERT embeddings were extracted from the youtube comments specified in Malayalam-English code mixed text. The extracted features were trained with different machine learning models and the models are evaluated using the development data and results are tabulated in Table 3 . From the Table 3 , count vectorizer feature with MLP classifier is giving the F1-score of 0.97.", "cite_spans": [], "ref_spans": [ { "start": 314, "end": 321, "text": "Table 3", "ref_id": null }, { "start": 333, "end": 340, "text": "Table 3", "ref_id": null } ], "eq_spans": [], "section": "Malayalam-English dataset", "sec_num": "5.2" }, { "text": "The features such as TFIDF, Count vectorizer and BERT embeddings were extracted from the youtube comments specified in Kannada-English code mixed text. The extracted features were trained with different machine learning models and the models are evaluated using the development data and results are tabulated in Table 4 .", "cite_spans": [], "ref_spans": [ { "start": 312, "end": 319, "text": "Table 4", "ref_id": "TABREF5" } ], "eq_spans": [], "section": "Kannada-English dataset", "sec_num": "5.3" }, { "text": "From the Table 3 , count vectorizer feature with MLP classifier is giving the F1-score of 0.69.", "cite_spans": [], "ref_spans": [ { "start": 9, "end": 16, "text": "Table 3", "ref_id": null } ], "eq_spans": [], "section": "Kannada-English dataset", "sec_num": "5.3" }, { "text": "Three runs were submitted using the Tamil-English, Malayalam-English, and Kannada-English code mixed text. The results of the runs were tabulated in Table in 5. From Table 5 , it has been noted that the performance of the proposed system is better for ", "cite_spans": [], "ref_spans": [ { "start": 149, "end": 160, "text": "Table in 5.", "ref_id": "TABREF6" }, { "start": 166, "end": 173, "text": "Table 5", "ref_id": "TABREF6" } ], "eq_spans": [], "section": "Kannada-English dataset", "sec_num": "5.3" }, { "text": "There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. The goal of this task is to identify offensive language content of the codemixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. In the proposed work, the basic TFIDF and count vectorizer features are giving better performance when compared to sentence embeddings. The examples are not sufficient to train the deep learning models. 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Association for Computational Linguistics.", "links": null } }, "ref_entries": { "TABREF1": { "type_str": "table", "num": null, "content": "", "html": null, "text": "" }, "TABREF3": { "type_str": "table", "num": null, "content": "
Features ClassifierPrecision Recall F1-score
Tfidfk-nearest0.910.910.91
TfidfMLP0.970.970.97
TfidfSVM0.870.750.81
Countvec k-nearest0.900.910.91
Countvec MLP0.970.970.97
Countvec SVM0.960.960.96
BERTMLP classifier 0.950.870.85
", "html": null, "text": "Performance of the proposed approach of Tamil-English code mixed text using dev data" }, "TABREF5": { "type_str": "table", "num": null, "content": "
DatasetPrecision Recall F1-score Rank
Tam-Eng 0.740.730.736
Mal-Eng 0.950.960.953
Kan-Eng 0.710.740.705
", "html": null, "text": "Performance of the proposed approach of Kannada-English code mixed text using dev data" }, "TABREF6": { "type_str": "table", "num": null, "content": "", "html": null, "text": "Performance of the proposed approach using test data Malayalam-English code mixed text." } } } }