Incorporating Semantic And Syntactic Information Into Document Representation For Document Clustering
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Successful Culturing Of Glover S Cancer Organism And Development Of Metastasizing Tumors In Animals Produced By Cultures From Human Malignancy
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Author :
language : en
Publisher:
Release Date : 1953
Successful Culturing Of Glover S Cancer Organism And Development Of Metastasizing Tumors In Animals Produced By Cultures From Human Malignancy written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1953 with categories.
Incorporating Semantic And Syntactic Information Into Document Representation For Document Clustering
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Author :
language : en
Publisher:
Release Date : 2005
Incorporating Semantic And Syntactic Information Into Document Representation For Document Clustering written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Document clustering is a widely used strategy for information retrieval and text data mining. In traditional document clustering systems, documents are represented as a bag of independent words. In this project, we propose to enrich the representation of a document by incorporating semantic information and syntactic information. Semantic analysis and syntactic analysis are performed on the raw text to identify this information. A detailed survey of current research in natural language processing, syntactic analysis, and semantic analysis is provided. Our experimental results demonstrate that incorporating semantic information and syntactic information can improve the performance of our document clustering system for most of our data sets. A statistically significant improvement can be achieved when we combine both syntactic and semantic information. Our experimental results using compound words show that using only compound words does not improve the clustering performance for our data sets. When the compound words are combined with original single words, the combined feature set gets slightly better performance for most data sets. But this improvement is not statistically significant. In order to select the best clustering algorithm for our document clustering system, a comparison of several widely used clustering algorithms is performed. Although the bisecting K-means method has advantages when working with large datasets, a traditional hierarchical clustering algorithm still achieves the best performance for our small datasets.
Enhancing Document Clustering By Integrating Semantic Background Knowledge And Syntactic Features Into The Bag Of Words Representation
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Author : Rayner Alfred
language : en
Publisher:
Release Date : 2011
Enhancing Document Clustering By Integrating Semantic Background Knowledge And Syntactic Features Into The Bag Of Words Representation written by Rayner Alfred and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Document clustering categories.
Handbook Of Research On Text And Web Mining Technologies
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Author : Song, Min
language : en
Publisher: IGI Global
Release Date : 2008-09-30
Handbook Of Research On Text And Web Mining Technologies written by Song, Min and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-30 with Computers categories.
Examines recent advances and surveys of applications in text and web mining which should be of interest to researchers and end-users alike.
Soft Computing Applications And Intelligent Systems
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Author : Shahrul Azman Noah
language : en
Publisher: Springer
Release Date : 2013-08-16
Soft Computing Applications And Intelligent Systems written by Shahrul Azman Noah and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-16 with Computers categories.
This book constitutes the refereed proceedings of the International Second International Multi-Conference on Artificial Intelligence Technology, M-CAIT 2013, held in Shah Alam, in August 2013. The 25 revised full papers presented were carefully reviewed and selected from 110 submissions. M-CAIT 2013 hosted four special tracks in a single event: Intelligence Computation on Pattern Analysis and Robotics (ICPAIR 2013), Data Mining and Optimization (DMO 2013), Semantic Technology and Information Retrieval (STAIR 2013) and Industrial Computing & Applied Informatics (IComp 2013). The papers address issues of state-of-the-art research, development, implementation and applications within the four focus areas in CAIT: pattern recognition, data mining and optimization, knowledge technology and industrial computing.
Advances In Knowledge Discovery And Data Mining
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Author : Joshua Zhexue Huang
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-09
Advances In Knowledge Discovery And Data Mining written by Joshua Zhexue Huang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-09 with Computers categories.
The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 revised full papers and 58 revised short papers were carefully reviewed and selected from 331 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, machine learning, artificial intelligence and pattern recognition, data warehousing and databases, statistics, knoweldge engineering, behavior sciences, visualization, and emerging areas such as social network analysis.
Dissertation Abstracts International
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Author :
language : en
Publisher:
Release Date : 2006
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Dissertations, Academic categories.
Incorporating Background Knowledge In Document Clustering
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Author : Samah Jamal Fodeh
language : en
Publisher:
Release Date : 2010
Incorporating Background Knowledge In Document Clustering written by Samah Jamal Fodeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Categories (Philosophy) categories.
Representation Learning For Natural Language Processing
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Author : Zhiyuan Liu
language : en
Publisher: Springer Nature
Release Date : 2020-07-03
Representation Learning For Natural Language Processing written by Zhiyuan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-03 with Computers categories.
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Deep Learning For Nlp And Speech Recognition
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Author : Uday Kamath
language : en
Publisher: Springer
Release Date : 2019-06-10
Deep Learning For Nlp And Speech Recognition written by Uday Kamath and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-10 with Computers categories.
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.