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Author: Shriphani Palakodety Publisher: Springer Nature ISBN: 9811656258 Category : Computers Languages : en Pages : 67
Book Description
This book focuses on methods that are unsupervised or require minimal supervision—vital in the low-resource domain. Over the past few years, rapid growth in Internet access across the globe has resulted in an explosion in user-generated text content in social media platforms. This effect is significantly pronounced in linguistically diverse areas of the world like South Asia, where over 400 million people regularly access social media platforms. YouTube, Facebook, and Twitter report a monthly active user base in excess of 200 million from this region. Natural language processing (NLP) research and publicly available resources such as models and corpora prioritize Web content authored primarily by a Western user base. Such content is authored in English by a user base fluent in the language and can be processed by a broad range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions features high levels of multilinguality, code-switching, and varied language skill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text. This book is designed for NLP practitioners well versed in recent advances in the field but unfamiliar with the landscape of low-resource multilingual NLP. The contents of this book introduce the various challenges associated with social media content, quantify these issues, and provide solutions and intuition. When possible, the methods discussed are evaluated on real-world social media data sets to emphasize their robustness to the noisy nature of the social media environment. On completion of the book, the reader will be well-versed with the complexity of text-mining in multilingual, low-resource environments; will be aware of a broad set of off-the-shelf tools that can be applied to various problems; and will be able to conduct sophisticated analyses of such text.
Author: Shriphani Palakodety Publisher: Springer Nature ISBN: 9811656258 Category : Computers Languages : en Pages : 67
Book Description
This book focuses on methods that are unsupervised or require minimal supervision—vital in the low-resource domain. Over the past few years, rapid growth in Internet access across the globe has resulted in an explosion in user-generated text content in social media platforms. This effect is significantly pronounced in linguistically diverse areas of the world like South Asia, where over 400 million people regularly access social media platforms. YouTube, Facebook, and Twitter report a monthly active user base in excess of 200 million from this region. Natural language processing (NLP) research and publicly available resources such as models and corpora prioritize Web content authored primarily by a Western user base. Such content is authored in English by a user base fluent in the language and can be processed by a broad range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions features high levels of multilinguality, code-switching, and varied language skill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text. This book is designed for NLP practitioners well versed in recent advances in the field but unfamiliar with the landscape of low-resource multilingual NLP. The contents of this book introduce the various challenges associated with social media content, quantify these issues, and provide solutions and intuition. When possible, the methods discussed are evaluated on real-world social media data sets to emphasize their robustness to the noisy nature of the social media environment. On completion of the book, the reader will be well-versed with the complexity of text-mining in multilingual, low-resource environments; will be aware of a broad set of off-the-shelf tools that can be applied to various problems; and will be able to conduct sophisticated analyses of such text.
Author: Shriphani Palakodety Publisher: ISBN: 9789811656262 Category : Languages : en Pages : 0
Book Description
This book focuses on methods that are unsupervised or require minimal supervision-vital in the low-resource domain. Over the past few years, rapid growth in Internet access across the globe has resulted in an explosion in user-generated text content in social media platforms. This effect is significantly pronounced in linguistically diverse areas of the world like South Asia, where over 400 million people regularly access social media platforms. YouTube, Facebook, and Twitter report a monthly active user base in excess of 200 million from this region. Natural language processing (NLP) research and publicly available resources such as models and corpora prioritize Web content authored primarily by a Western user base. Such content is authored in English by a user base fluent in the language and can be processed by a broad range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions features high levels of multilinguality, code-switching, and varied language skill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text. This book is designed for NLP practitioners well versed in recent advances in the field but unfamiliar with the landscape of low-resource multilingual NLP. The contents of this book introduce the various challenges associated with social media content, quantify these issues, and provide solutions and intuition. When possible, the methods discussed are evaluated on real-world social media data sets to emphasize their robustness to the noisy nature of the social media environment. On completion of the book, the reader will be well-versed with the complexity of text-mining in multilingual, low-resource environments; will be aware of a broad set of off-the-shelf tools that can be applied to various problems; and will be able to conduct sophisticated analyses of such text.
Author: Anand Kumar M Publisher: Springer Nature ISBN: 3031332318 Category : Computers Languages : en Pages : 362
Book Description
This book constitutes refereed proceedings from the First International Conference on Speech and Language Technologies for Low-resource Languages, SPELLL 2022, held in Kalavakkam, India, in November 2022. The 25 presented papers were thoroughly reviewed and selected from 70 submissions. The papers are organised in the following topical sections: language resources; language technologies; speech technologies; multimodal data analysis; fake news detection in low-resource languages (regional-fake); low resource cross-domain, cross-lingualand cross-modal offensie content analysis (LC4).
Author: Federico Alberto Pozzi Publisher: Morgan Kaufmann ISBN: 0128044381 Category : Computers Languages : en Pages : 284
Book Description
The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network mining Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics
Author: Pakray, Partha Publisher: IGI Global ISBN: Category : Computers Languages : en Pages : 328
Book Description
In our increasingly interconnected world, low-resource languages face the threat of oblivion. These linguistic gems, often spoken by marginalized communities, are at risk of fading away due to limited data and resources. The neglect of these languages not only erodes cultural diversity but also hinders effective communication, education, and social inclusion. Academics, practitioners, and policymakers grapple with the urgent need for a comprehensive solution to preserve and empower these vulnerable languages. Empowering Low-Resource Languages With NLP Solutions is a pioneering book that stands as the definitive answer to the pressing problem at hand. It tackles head-on the challenges that low-resource languages face in the realm of Natural Language Processing (NLP). Through real-world case studies, expert insights, and a comprehensive array of topics, this book equips its readersacademics, researchers, practitioners, and policymakerswith the tools, strategies, and ethical considerations needed to address the crisis facing low-resource languages.
Author: Parikshit N Mahalle Publisher: CRC Press ISBN: 1040031137 Category : Computers Languages : en Pages : 309
Book Description
This book explores the need for a data‐centric AI approach and its application in the multidisciplinary domain, compared to a model‐centric approach. It examines the methodologies for data‐centric approaches, the use of data‐centric approaches in different domains, the need for edge AI and how it differs from cloud‐based AI. It discusses the new category of AI technology, "data‐centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data‐centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. • Includes a collection of case studies with experimentation results to adhere to the practical approaches • Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways • Discusses methodologies to achieve accurate results by improving the quality of data • Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications
Author: Fouad Sabry Publisher: One Billion Knowledgeable ISBN: Category : Computers Languages : en Pages : 131
Book Description
What Is Text Mining Text mining, also known as text data mining (TDM) or text analytics, is the technique of extracting useful information from text. Related terms include text data mining (TDM) and text analytics. It is "the discovery by computer of new, previously unknown information by automatically extracting information from various written resources," according to one definition of the term. Websites, books, emails, reviews, and articles are all examples of written materials that may be utilized. Typically, the best way to acquire high-quality information is to construct patterns and trends through the use of methods such as statistical pattern learning. According to Hotho et al. (2005), we are able to differentiate between three distinct perspectives of text mining. These perspectives are information extraction, data mining, and a process known as knowledge discovery in databases (KDD). Text mining often entails the process of structuring the text that is input, determining patterns within the data that has been structured, and then lastly evaluating and interpreting the result of the mining process. When discussing text mining, the term "high quality" typically relates to some combination of the concepts of relevance, novelty, and interest. Text categorization, text clustering, concept/entity extraction, generation of granular taxonomies, sentiment analysis, document summarizing, and entity relation modeling are all examples of typical text mining activities. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Text Mining Chapter 2: Natural Language Processing Chapter 3: Data Mining Chapter 4: Information Extraction Chapter 5: Semantic Similarity Chapter 6: Unstructured Data Chapter 7: Biomedical Text Mining Chapter 8: Sentiment Analysis Chapter 9: Word Embedding Chapter 10: Social Media Mining (II) Answering the public top questions about text mining. (III) Real world examples for the usage of text mining in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of text mining' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of text mining.
Author: Gabe Ignatow Publisher: SAGE Publications ISBN: 150633699X Category : Computers Languages : en Pages : 345
Book Description
Students in social science courses communicate, socialize, shop, learn, and work online. When they are asked to collect data for course projects they are often drawn to social media platforms and other online sources of textual data. There are many software packages and programming languages available to help students collect data online, and there are many texts designed to help with different forms of online research, from surveys to ethnographic interviews. But there is no textbook available that teaches students how to construct a viable research project based on online sources of textual data such as newspaper archives, site user comment archives, digitized historical documents, or social media user comment archives. Gabe Ignatow and Rada F. Mihalcea's new text An Introduction to Text Mining will be a starting point for undergraduates and first-year graduate students interested in collecting and analyzing textual data from online sources, and will cover the most critical issues that students must take into consideration at all stages of their research projects, including: ethical and philosophical issues; issues related to research design; web scraping and crawling; strategic data selection; data sampling; use of specific text analysis methods; and report writing.
Author: Shriphani Palakodety
Author: Shriphani Palakodety
Author: Shriphani Palakodety
Author: Bharathi Raja Chakravarthi
Author: Anand Kumar M
Author: Federico Alberto Pozzi
Author: Pakray, Partha
Author: Dmitry I. Ignatov
Author: Parikshit N Mahalle
Author: Fouad Sabry
Author: Gabe Ignatow