Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Hierarchical Text Classification Of Large Scale Topics
Download Hierarchical Text Classification Of Large Scale Topics full books in PDF, epub, and Kindle. Read online Hierarchical Text Classification Of Large Scale Topics ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Hierarchical Text Classification of Large-scale Topics by : Koustuv Sinha
Download or read book Hierarchical Text Classification of Large-scale Topics written by Koustuv Sinha and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Topic modelling and classification of documents is a well studied problem in Natural Language understanding. Deep neural networks have displayed superior performance over the traditional supervised classifiers in text classification. They learn to extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge of topic category taxonomy to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models with significantly fewer computational resources while maintaining high interpretability." --
Book Synopsis Large Scale Hierarchical Classification: State of the Art by : Azad Naik
Download or read book Large Scale Hierarchical Classification: State of the Art written by Azad Naik and published by Springer. This book was released on 2018-10-09 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: 1. High imbalance between classes at different levels of the hierarchy 2. Incorporating relationships during model learning leads to optimization issues 3. Feature selection 4. Scalability due to large number of examples, features and classes 5. Hierarchical inconsistencies 6. Error propagation due to multiple decisions involved in making predictions for top-down methods The brief also demonstrates how multiple hierarchies can be leveraged for improving the HC performance using different Multi-Task Learning (MTL) frameworks. The purpose of this book is two-fold: 1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques. 2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC. New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.
Book Synopsis Hierarchical Classification and Its Application in University Search by : Xiao Li
Download or read book Hierarchical Classification and Its Application in University Search written by Xiao Li and published by . This book was released on 2013 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Web search engines have been adopted by most universities fo r searching webpages in their own domains. Basically, a user sends keywords to the search e ngine and the search engine returns a flat ranked list of webpages. However, in universit y search, user queries are usually related to topics. Simple keyword queries are often insu ffi cient to express topics as keywords. On the other hand, most E-commerce sites allow users to brows e and search products in various hierarchies. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined for university search engines. The main di ffi culty is to automatically classify and rank a massive number of webpages into the topic hierarchies for universities. In this thesis, we use machine learning and data mining techn iques to build a novel hybrid search engine with integrated hierarchies for universitie s, called SEEU (S earch E ngine with hi E rarchy for U niversities). Firstly, we study the problem of e ff ective hierarchical webpage classification. We develop a parallel webpage classification system based on Support Ve ctor Machines. With extensive experiments on the well-known ODP (Open Directory Project) dataset, we empirically demon- strate that our hierarchical classification system is very e ff ective and outperforms the traditional flat classification approaches significantly. Secondly, we study the problem of integrating hierarchical classification into the ranking system of keywords-based search engines. We propose a novel ranking framework, called ERIC (E nhanced R anking by h I erarchical C lassification), for search engines with hierarchies. Experimental results on four large-scale TREC (Text REtrie val Conference) web search datasets show that our ranking system with hierarchical classificati on outperforms the traditional flat keywords-based search methods significantly. Thirdly, we propose a novel active learning framework to imp rove the performance of hi- erarchical classification, which is important for ranking w ebpages in hierarchies. From our experiments on the benchmark text datasets, we find that our a ctive learning framework can achieve good classification performance yet save a consider able number of labeling e ff ort com- pared with the state-of-the-art active learning methods fo r hierarchical text classification. Fourthly, based on the proposed classification and ranking m ethods, we present a novel hierarchical classification framework for mining academic topics from university webpages. We build an academic topic hierarchy based on the commonly ac cepted Wikipedia academic disciplines. Based on this hierarchy, we train a hierarchic al classifier and apply it to mine academic topics. According to our comprehensive analysis, the academic topics mined by our method are reasonable and consistent with the real-world to pic distribution in universities. Finally, we combine all the proposed techniques together an d implement the SEEU search engine. According to two usability studies conducted in the ECE and the CS departments at our university, SEEU is favored by the majority of participa nts. To conclude, the main contribution of this thesis is a novel s earch engine, called SEEU, for universities. We discuss the challenges toward building SE EU and propose e ff ective machine learning and data mining methods to tackle them. With extens ive experiments on well-known benchmark datasets and real-world university webpage data sets, we demonstrate that our sys- tem is very e ff ective. In addition, two usability studies of SEEU in our uni versity show that SEEU has a great promise for university search.
Book Synopsis Practical Natural Language Processing by : Sowmya Vajjala
Download or read book Practical Natural Language Processing written by Sowmya Vajjala and published by O'Reilly Media. This book was released on 2020-06-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
Book Synopsis Mining Text Data by : Charu C. Aggarwal
Download or read book Mining Text Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2012-02-03 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
Book Synopsis The Big Book of Concepts by : Gregory Murphy
Download or read book The Big Book of Concepts written by Gregory Murphy and published by MIT Press. This book was released on 2004-01-30 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concepts embody our knowledge of the kinds of things there are in the world. Tying our past experiences to our present interactions with the environment, they enable us to recognize and understand new objects and events. Concepts are also relevant to understanding domains such as social situations, personality types, and even artistic styles. Yet like other phenomenologically simple cognitive processes such as walking or understanding speech, concept formation and use are maddeningly complex. Research since the 1970s and the decline of the "classical view" of concepts have greatly illuminated the psychology of concepts. But persistent theoretical disputes have sometimes obscured this progress. The Big Book of Concepts goes beyond those disputes to reveal the advances that have been made, focusing on the major empirical discoveries. By reviewing and evaluating research on diverse topics such as category learning, word meaning, conceptual development in infants and children, and the basic level of categorization, the book develops a much broader range of criteria than is usual for evaluating theories of concepts.
Book Synopsis The Science of Science by : Dashun Wang
Download or read book The Science of Science written by Dashun Wang and published by Cambridge University Press. This book was released on 2021-03-25 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive overview of the exciting field of the 'science of science'. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise.
Book Synopsis The Semantic Web by : Pascal Hitzler
Download or read book The Semantic Web written by Pascal Hitzler and published by Springer. This book was released on 2019-05-24 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th International Semantic Web Conference, ESWC 2019, held in Portorož, Slovenia. The 39 revised full papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in three tracks: research track, resources track, and in-use track and deal with the following topical areas: distribution and decentralisation, velocity on the Web, research of research, ontologies and reasoning, linked data, natural language processing and information retrieval, semantic data management and data infrastructures, social and human aspects of the Semantic Web, and, machine learning.
Book Synopsis Current Challenges in Patent Information Retrieval by : Mihai Lupu
Download or read book Current Challenges in Patent Information Retrieval written by Mihai Lupu and published by Springer. This book was released on 2017-03-24 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition provides a systematic introduction to the work and views of the emerging patent-search research and innovation communities as well as an overview of what has been achieved and, perhaps even more importantly, of what remains to be achieved. It revises many of the contributions of the first edition and adds a significant number of new ones. The first part “Introduction to Patent Searching” includes two overview chapters on the peculiarities of patent searching and on contemporary search technology respectively, and thus sets the scene for the subsequent parts. The second part on “Evaluating Patent Retrieval” then begins with two chapters dedicated to patent evaluation campaigns, followed by two chapters discussing complementary issues from the perspective of patent searchers and from the perspective of related domains, notably legal search. “High Recall Search” includes four completely new chapters dealing with the issue of finding only the relevant documents in a reasonable time span. The last (and with six papers the largest) part on “Special Topics in Patent Information Retrieval” covers a large spectrum of research in the patent field, from classification and image processing to translation. Lastly, the book is completed by an outlook on open issues and future research. Several of the chapters have been jointly written by intellectual property and information retrieval experts. However, members of both communities with a background different to that of the primary author have reviewed the chapters, making the book accessible to both the patent search community and to the information retrieval research community. It also not only offers the latest findings for academic researchers, but is also a valuable resource for IP professionals wanting to learn about current IR approaches in the patent domain.
Book Synopsis Inductive Inference for Large Scale Text Classification by : Catarina Silva
Download or read book Inductive Inference for Large Scale Text Classification written by Catarina Silva and published by Springer Science & Business Media. This book was released on 2009-11-13 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text classification is becoming a crucial task to analysts in different areas. In the last few decades, the production of textual documents in digital form has increased exponentially. Their applications range from web pages to scientific documents, including emails, news and books. Despite the widespread use of digital texts, handling them is inherently difficult - the large amount of data necessary to represent them and the subjectivity of classification complicate matters. This book gives a concise view on how to use kernel approaches for inductive inference in large scale text classification; it presents a series of new techniques to enhance, scale and distribute text classification tasks. It is not intended to be a comprehensive survey of the state-of-the-art of the whole field of text classification. Its purpose is less ambitious and more practical: to explain and illustrate some of the important methods used in this field, in particular kernel approaches and techniques.
Book Synopsis Predictive Clustering by : Hendrik Blockeel
Download or read book Predictive Clustering written by Hendrik Blockeel and published by Springer. This book was released on 2012-05-31 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques. The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics. The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.
Book Synopsis Multidimensional Mining of Massive Text Data by : Chao Zhang
Download or read book Multidimensional Mining of Massive Text Data written by Chao Zhang and published by Springer Nature. This book was released on 2022-06-01 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.
Book Synopsis Text Classification Using Hierarchical Structures by : Ahmad Saeed Khattak
Download or read book Text Classification Using Hierarchical Structures written by Ahmad Saeed Khattak and published by . This book was released on 2013 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Information Retrieval by : Pavel Serdyukov
Download or read book Advances in Information Retrieval written by Pavel Serdyukov and published by Springer. This book was released on 2013-03-12 with total page 919 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 35th European Conference on IR Research, ECIR 2013, held in Moscow, Russia, in March 2013. The 55 full papers, 38 poster papers and 10 demonstrations presented in this volume were carefully reviewed and selected from 287 submissions. The papers are organized in the following topical sections: user aspects; multimedia and cross-media IR; data mining; IR theory and formal models; IR system architectures; classification; Web; event detection; temporal IR, and microblog search. Also included are 4 tutorial and 2 workshop presentations.
Book Synopsis Experimental IR Meets Multilinguality, Multimodality, and Interaction by : Norbert Fuhr
Download or read book Experimental IR Meets Multilinguality, Multimodality, and Interaction written by Norbert Fuhr and published by Springer. This book was released on 2016-08-22 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference of the CLEF Initiative, CLEF 2016, held in Toulouse, France, in September 2016. The 10 full papers and 8 short papers presented together with 5 best of the labs papers were carefully reviewed and selected from 36 submissions. In addition to these talks, this volume contains the results of 7 benchmarking labs reporting their year long activities in overview talks and lab sessions. The papers address all aspects of information access in any modality and language and cover a broad rangeof topics in the fields of multilingual and multimodal information access evaluation.
Book Synopsis Neural Information Processing by : Teddy Mantoro
Download or read book Neural Information Processing written by Teddy Mantoro and published by Springer Nature. This book was released on 2021-12-06 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows: Part I: Theory and algorithms; Part II: Theory and algorithms; human centred computing; AI and cybersecurity; Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications; Part IV: Applications.
Book Synopsis Advances in Information Retrieval by : Paul Clough
Download or read book Advances in Information Retrieval written by Paul Clough and published by Springer. This book was released on 2011-04-12 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 33rd annual European Conference on Information Retrieval Research, ECIR 2011, held in Dublin, Ireland, in April 2010. The 45 revised full papers presented together with 24 poster papers, 17 short papers, and 6 tool demonstrations were carefully reviewed and selected from 223 full research paper submissions and 64 poster/demo submissions. The papers are organized in topical sections on text categorization, recommender systems, Web IR, IR evaluation, IR for Social Networks, cross-language IR, IR theory, multimedia IR, IR applications, interactive IR, and question answering /NLP.
