{"title":"Chengxiang Zhai","description":null,"products":[{"product_id":"text-data-management-and-analysis-book-chengxiang-zhai-9781970001198","title":"Text Data Management and Analysis","description":"Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic.\u003cbr\u003e \u003cbr\u003e This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50404432216337,"sku":"CIN1970001194G","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ NEW \/ INGRAM","offer_id":51063490511121,"sku":"NIN9781970001198","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1970001194.jpg?v=1750868881"},{"product_id":"statistical-language-models-for-information-retrieval-book-chengxiang-zhai-9781601981868","title":"Statistical Language Models for Information Retrieval","description":"Statistical Language Models for Information Retrieval systematically and critically reviews the existing work in applying statistical language models to information retrieval, summarizes their contributions, and points out outstanding challenges. Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that statistical language models not only lead to superior empirical performance, but also facilitate parameter tuning and open up possibilities for modeling non-traditional retrieval problems. In general, statistical language models provide a principled way of modeling various kinds of retrieval problems.  Tho book reviews the development of this language modeling approach. It surveys a wide range of retrieval models based on language modeling and attempts to make connections between this new family of models and traditional retrieval models. It summarizes the progress made so far in these models and point out remaining challenges to be solved to further increase their impact. It is written for readers who already have some basic knowledge about information retrieval. Some knowledge of probability and statistics such as the maximum likelihood estimator is helpful, but not a prerequisite to understanding the high-level discussion.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51038226317585,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51038229168401,"sku":"NIN9781601981868","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1601981864.jpg?v=1751310497"},{"product_id":"statistical-language-models-for-information-retrieval-book-chengxiang-zhai-9783031010026","title":"Statistical Language Models for Information Retrieval","description":"Statistical Language Models for Information Retrieval systematically and critically reviews the existing work in applying statistical language models to information retrieval, summarizes their contributions, and points out outstanding challenges. Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that statistical language models not only lead to superior empirical performance, but also facilitate parameter tuning and open up possibilities for modeling non-traditional retrieval problems. In general, statistical language models provide a principled way of modeling various kinds of retrieval problems. Statistical Language Models for Information Retrieval reviews the development of this language modeling approach. It surveys a wide range of retrieval models based on language modeling and attempts to make connections between this new family of models and traditional retrieval models. It summarizes the progress made so far in these models and point out remaining challenges to be solved to further increase their impact. Statistical Language Models for Information Retrieval is written for readers who already have some basic knowledge about information retrieval. Some knowledge of probability and statistics such as the maximum likelihood estimator is helpful, but not a prerequisite to understanding the high-level discussion.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52431733588241,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52431734210833,"sku":"NLS9783031010026","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783031010026.jpg?v=1759173368"}],"url":"https:\/\/www.worldofbooks.com\/en-au\/collections\/author-books-by-chengxiang-zhai.oembed","provider":"World of Books ","version":"1.0","type":"link"}