Sana Kidwai

Bio: Sana Kidwai is an academic researcher from University of Cambridge. The author has contributed to research in topics: Language identification & Social media. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Automatic Language Identification in Code-Switched Hindi-English Social Media Text

Abstract: Natural Language Processing (NLP) tools typically struggle to process code-switched data and so linguists are commonly forced to annotate such data manually. As this data becomes more readily available, automatic tools are increasingly needed to help speed up the annotation process and improve consistency. Last year, such a toolkit was developed to semi-automatically annotate transcribed bilingual code-switched Vietnamese-English speech data with token-based language information and POS tags (hereafter the CanVEC toolkit, L. Nguyen & Bryant, 2020). In this work, we extend this methodology to another language pair, Hindi-English, to explore the extent to which we can standardise the automation process. Specifically, we applied the principles behind the CanVEC toolkit to data from the International Conference on Natural Language Processing (ICON) 2016 shared task, which consists of social media posts (Facebook, Twitter and WhatsApp) that have been annotated with language and POS tags (Molina et al., 2016). We used the ICON-2016 annotations as the gold-standard labels in the language identification task. Ultimately, our tool achieved an F1 score of 87.99% on the ICON-2016 data. We then evaluated the first 500 tokens of each social media subset manually, and found almost 40% of all errors were caused entirely by problems with the gold-standard, i.e., our system was correct. It is thus likely that the overall accuracy of our system is higher than reported. This shows great potential for effectively automating the annotation of code-switched corpora, on different language combinations, and in different genres. We finally discuss some limitations of our approach and release our code and human evaluation together with this paper.

CanVEC - the Canberra Vietnamese-English Code-switching Natural Speech Corpus.

Abstract: This paper introduces the Canberra Vietnamese-English Code-switching corpus (CanVEC), an original corpus of natural mixed speech that we semi-automatically annotated with language information, part of speech (POS) tags and Vietnamese translations. The corpus, which was built to inform a sociolinguistic study on language variation and code-switching, consists of 10 hours of recorded speech (87k tokens) between 45 Vietnamese-English bilinguals living in Canberra, Australia. We describe how we collected and annotated the corpus by pipelining several monolingual toolkits to considerably speed up the annotation process. We also describe how we evaluated the automatic annotations to ensure corpus reliability. We make the corpus available for research purposes.

A Systematic Review on Language Identification of Code-Mixed Text: Techniques, Data Availability, Challenges, and Framework Development

Abstract: The mix of native language with other languages (code-mixing) in social media has posed a severe challenge for language identification (LID) systems. It has encouraged research on code-mixed LID solutions. Four things have been identified in this study, such as techniques, challenges, and dataset availability with corresponding quality criteria and developed a comprehensive framework for code-mixed LID. Also, we identified gaps and future work opportunities in tackling code-mixed LID challenges. Based on our analysis of reviewed studies, we outlined key points for future research in code-mixed LID. We demonstrated a taxonomy of applied techniques for code-mixed LID and highlighted the different technique variants. In code-mixed LID tasks, we discovered four significant challenges: ambiguity, lexical borrowing, non-standard words, and intra-word code-mixing. This systematic literature review recognised 32 code-mixed datasets available for LID. We proposed five features to describe the quality criteria datasets, such as the number of instances or sentences, percentage of code-mixed types in the data, number of tokens, number of unique tokens, and average sentence length. Finally, we synthesised the methodologies and proposed a conceptual framework for subsequent studies through our literature analysis.

A Systematic Review on Language Identification of Code-Mixed Text: Techniques, Data Availability, Challenges, and Framework Development

Abstract: The mix of native language with other languages (code-mixing) in social media has posed a severe challenge for language identification (LID) systems. It has encouraged research on code-mixed LID solutions. This study investigated the techniques, challenges, and dataset availability with corresponding quality criteria and developed a comprehensive framework for code-mixed LID. This study addressed four research issues to identify gaps and future work opportunities in tackling code-mixed LID challenges. Based on our analysis of reviewed studies, we outlined key points for future research in code-mixed LID. We demonstrated a taxonomy of applied techniques for code-mixed LID and highlighted the different technique variants. In code-mixed LID tasks, we discovered four significant challenges: ambiguity, lexical borrowing, non-standard words, and intra-word code-mixing. This systematic literature review recognised 32 code-mixed datasets available for LID. We proposed five features to describe the quality criteria dataset. The features are the number of instances or sentences, percentage of code-mixed types in the data, number of tokens, number of unique tokens, and average sentence length. Finally, we synthesised the methodologies and proposed a conceptual framework for subsequent studies through our literature analysis.

Corpus creation and language identification for code-mixed Indonesian-Javanese-English Tweets

Abstract: With the massive use of social media today, mixing between languages in social media text is prevalent. In linguistics, the phenomenon of mixing languages is known as code-mixing. The prevalence of code-mixing exposes various concerns and challenges in natural language processing (NLP), including language identification (LID) tasks. This study presents a word-level language identification model for code-mixed Indonesian, Javanese, and English tweets. First, we introduce a code-mixed corpus for Indonesian-Javanese-English language identification (IJELID). To ensure reliable dataset annotation, we provide full details of the data collection and annotation standards construction procedures. Some challenges encountered during corpus creation are also discussed in this paper. Then, we investigate several strategies for developing code-mixed language identification models, such as fine-tuning BERT, BLSTM-based, and CRF. Our results show that fine-tuned IndoBERTweet models can identify languages better than the other techniques. This is the result of BERT’s ability to understand each word’s context from the given text sequence. Finally, we show that sub-word language representation in BERT models can provide a reliable model for identifying languages in code-mixed texts.