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Elizabeth Sherly

Bio: Elizabeth Sherly is an academic researcher from Indian Institute of Information Technology and Management, Kerala. The author has contributed to research in topics: Malayalam & Sentiment analysis. The author has an hindex of 10, co-authored 60 publications receiving 387 citations. Previous affiliations of Elizabeth Sherly include Indian Institute of Information Technology and Management, Gwalior & University of York.


Papers
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Proceedings ArticleDOI
16 Dec 2020
TL;DR: The Dravidian-CodeMix-FIRE 2020 Track as discussed by the authors focused on sentiment analysis of code-mixed text in code mixed text for Tamil and Malayalam, and participants were given a dataset of YouTube comments and the goal of the shared task submissions was to recognise the sentiment of each comment by classifying them into positive, negative, neutral, mixed-feeling classes or by recognizing whether the comment is not in the intended language.
Abstract: Sentiment analysis of Dravidian languages has received attention in recent years However, most social media text is code-mixed and there is no research available on sentiment analysis of code-mixed Dravidian languages The Dravidian-CodeMix-FIRE 2020, a track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text, focused on creating a platform for researchers to come together and investigate the problem There were two languages for this track: (i) Tamil, and (ii) Malayalam The participants were given a dataset of YouTube comments and the goal of the shared task submissions was to recognise the sentiment of each comment by classifying them into positive, negative, neutral, mixed-feeling classes or by recognising whether the comment is not in the intended language The performance of the systems was evaluated by weighted-F1 score

87 citations

Proceedings ArticleDOI
06 Mar 2020
TL;DR: A set of quality metrics to evaluate the dataset and categorize them accordingly is proposed and will assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc.
Abstract: Code switching is a prevalent phenomenon in the multilingual community and social media interaction. In the past ten years, we have witnessed an explosion of code switched data in the social media that brings together languages from low resourced languages to high resourced languages in the same text, sometimes written in a non-native script. This increases the demand for processing code-switched data to assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc. The available corpora for code switching research played a major role in advancing this area of research. In this paper, we propose a set of quality metrics to evaluate the dataset and categorize them accordingly.

75 citations

Proceedings ArticleDOI
11 May 2020
TL;DR: In this article, a model of language models for minority and historical languages was developed using a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 P2 (Insight 2), co-funded by the European Regional Development Fund as well as the EU H2020 programme under grant agreements 731015 (ELEXIS-European Lexical Infrastructure), 825182 (Pret- ˆ a-LLOD), and IRCLA/2017/129 (CARDAMOM-Comparative Deep Models of Language
Abstract: This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight), SFI/12/RC/2289 P2 (Insight 2), co-funded by the European Regional Development Fund as well as by the EU H2020 programme under grant agreements 731015 (ELEXIS-European Lexical Infrastructure), 825182 (Pret- ˆ a-LLOD), and Irish Research Council ` grant IRCLA/2017/129 (CARDAMOM-Comparative Deep Models of Language for Minority and Historical Languages).

57 citations

Journal ArticleDOI
TL;DR: This work combines Robust Outlyingness Ratio (ROR) mechanism with extended NL-Means (ROR-NLM) filter based on Discrete Cosine Transform (DCT) for the detection and removal of noise.

51 citations

Posted Content
TL;DR: A new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators is presented, which obtained a Krippendorff’s alpha above 0.8 for the dataset.
Abstract: There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff's alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.

41 citations


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Journal ArticleDOI
TL;DR: This is it, the handbook of data mining and knowledge discovery that will be your best choice for better reading book that you will not spend wasted by reading this website.
Abstract: Give us 5 minutes and we will show you the best book to read today. This is it, the handbook of data mining and knowledge discovery that will be your best choice for better reading book. Your five times will not spend wasted by reading this website. You can take the book as a source to make better concept. Referring the books that can be situated with your needs is sometime difficult. But here, this is so easy. You can find the best thing of book that you can read.

252 citations

Proceedings ArticleDOI
11 May 2020
TL;DR: A gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube is created and inter-annotator agreement is presented, and the results of sentiment analysis trained on this corpus are shown.
Abstract: Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.

168 citations

Journal ArticleDOI
TL;DR: This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations.
Abstract: Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.

156 citations

Journal ArticleDOI
TL;DR: A novel type of soft rough covering is introduced by means of soft neighborhoods, and then it is used to improve decision making in a multicriteria group environment.
Abstract: In this paper, we contribute to a recent and successful modelization of uncertainty, which the practitioner often encounters in the formulation of multicriteria group decision making problems. To be precise, in order to approach the uncertainty issue we introduce a novel type of soft rough covering by means of soft neighborhoods, and then we use it to improve decision making in a multicriteria group environment. Our research method is as follows. Firstly we introduce the soft covering upper and lower approximation operators of soft rough coverings. Then its relationships with well-established types of soft rough coverings are analyzed. Secondly, we define and investigate the measure degree of our novel soft rough covering. With this tool we produce a new class of soft rough sets. Finally, we propose an application of such soft rough covering model to multicriteria group decision making by means of an algorithmic solution. A fully developed example supports the implementability of this decision making method.

147 citations