Enhancing Document Clustering By Integrating Semantic Background Knowledge And Syntactic Features Into The Bag Of Words Representation
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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.
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.
From Data And Information Analysis To Knowledge Engineering
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Author : Myra Spiliopoulou
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-20
From Data And Information Analysis To Knowledge Engineering written by Myra Spiliopoulou 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 2006-04-20 with Language Arts & Disciplines categories.
This volume collects revised versions of papers presented at the 29th Annual Conference of the Gesellschaft für Klassifikation, the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to traditional subjects like Classification, Clustering, and Data Analysis, converage extends to a wide range of topics relating to Computer Science: Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization.
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.
High Performance Text Document Clustering
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Author : Yanjun Li
language : en
Publisher:
Release Date : 2007
High Performance Text Document Clustering written by Yanjun Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Algorithms categories.
Data mining, also known as knowledge discovery in database (KDD), is the process to discover interesting unknown knowledge from a large amount of data. Text mining is to apply data mining techniques to extract interesting and nontrivial information and knowledge from unstructured text. Text clustering is one of important techniques of text mining, which is the unsupervised classification of similar documents into different groups. This research focuses on improving the performance of text clustering. We investigated the text clustering algorithms in four aspects: document representation, documents closeness measurement, high dimension reduction and parallelization. We propose a group of high performance text clustering algorithms, which target the unique characteristics of unstructured text database. First, two new text clustering algorithms are proposed. Unlike the vector space model, which treats document as a bag of words, we use a document representation which keeps the sequential relationship between words in the documents. In these two algorithms, the dimension of the database is reduced by considering the frequent word (meaning) sequences, and the closeness of two documents is measured based on the sharing of frequent word (meaning) sequences. Second, a text clustering algorithm with feature selection is proposed. This algorithm gradually reduces the high dimension of database by performing feature selection during the clustering. The new feature selection method applied is based on the well-known chi-square statistic and a new statistical data which can measure the positive and negative term-category dependence. Third, a group of new text clustering algorithms is developed based on the k-means algorithm. Instead of using the cosine function, a new function involving global information is proposed to measure the closeness between two documents. This new function utilizes the neighbor matrix introduced in [Guha:2000]. A new method for selecting initial centroids and a new heuristic function for selecting a cluster to split are adopted in the proposed algorithms. Last, a new parallel algorithm for bisecting k-means is proposed for the message-passing multiprocessor systems. This new algorithm, named PBKP, fully utilizes the data-parallelism of the bisecting k-means algorithm, and adopts a prediction step to balance the workloads of multiple processors to achieve a high speedup. Comprehensive performance studies were conducted on all the proposed algorithms. In order to evaluate the performance of these algorithms, we compared them with existing text clustering algorithms, such as k-means, bisecting k-means [Steinbach:2000] and FIHC [Fung:2003]. The experimental results show that our clustering algorithms are scalable and have much better clustering accuracy than existing algorithms. For the parallel PBKP algorithm, we tested it on a 9-node Linux cluster system and analyzed its performance. The experimental results suggest that the speedup of PBKP is linear with the number of processors and data points. Moreover, PBKP scales up better than the parallel k-means with respect to the desired number of clusters.
Knowledge Discovery Knowledge Engineering And Knowledge Management
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Author : Ana Fred
language : en
Publisher: Springer
Release Date : 2013-04-10
Knowledge Discovery Knowledge Engineering And Knowledge Management written by Ana Fred and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-10 with Computers categories.
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management, IC3K 2011, held in Paris, France, in October 2011. This book includes revised and extended versions of a strict selection of the best papers presented at the conference; 39 revised full papers together with one invited lecture were carefully reviewed and selected from 429 submissions. According to the three covered conferences KDIR 2011, KEOD 2011, and KMIS 2011, the papers are organized in topical sections on knowledge discovery and information retrieval, knowledge engineering and ontology development, and on knowledge management and information sharing.
Integrating Structure And Meaning
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Author : Jonathan Michael Fishbein
language : en
Publisher:
Release Date : 2008
Integrating Structure And Meaning written by Jonathan Michael Fishbein and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Current representation schemes for automatic text classification treat documents as syntactically unstructured collections of words (Bag-of-Words) or 'concepts' (Bag-of-Concepts). Past attempts to encode syntactic structure have treated part-of-speech information as another word-like feature, but have been shown to be less effective than non-structural approaches. We propose a new representation scheme using Holographic Reduced Representations (HRRs) as a technique to encode both semantic and syntactic structure, though in very different ways. This method is unique in the literature in that it encodes the structure across all features of the document vector while preserving text semantics. Our method does not increase the dimensionality of the document vectors, allowing for efficient computation and storage. We present the results of various Support Vector Machine classification experiments that demonstrate the superiority of this method over Bag-of-Concepts representations and improvement over Bag-of-Words in certain classification contexts.
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.
Semantically Enhanced Document Clustering
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Author : Ivan Stankov
language : en
Publisher:
Release Date : 2013
Semantically Enhanced Document Clustering written by Ivan Stankov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
This thesis advocates the view that traditional document clustering could be significantly improved by representing documents at different levels of abstraction at which the similarity between documents is considered. The improvement is with regard to the alignment of the clustering solutions to human judgement. The proposed methodology employs semantics with which the conceptual similarity be-tween documents is measured. The goal is to design algorithms which implement the meth-odology, in order to solve the following research problems: (i) how to obtain multiple deter-ministic clustering solutions; (ii) how to produce coherent large-scale clustering solutions across domains, regardless of the number of clusters; (iii) how to obtain clustering solutions which align well with human judgement; and (iv) how to produce specific clustering solu-tions from the perspective of the user's understanding for the domain of interest. The developed clustering methodology enhances separation between and improved coher-ence within clusters generated across several domains by using levels of abstraction. The methodology employs a semantically enhanced text stemmer, which is developed for the pur-pose of producing coherent clustering, and a concept index that provides generic document representation and reduced dimensionality of document representation. These characteristics of the methodology enable addressing the limitations of traditional text document clustering by employing computationally expensive similarity measures such as Earth Mover's Distance (EMD), which theoretically aligns the clustering solutions closer to human judgement. A threshold for similarity between documents that employs many-to-many similarity matching is proposed and experimentally proven to benefit the traditional clustering algorithms in pro-ducing clustering solutions aligned closer to human judgement. 4 The experimental validation demonstrates the scalability of the semantically enhanced document clustering methodology and supports the contributions: (i) multiple deterministic clustering solutions and different viewpoints to a document collection are obtained; (ii) the use of concept indexing as a document representation technique in the domain of document clustering is beneficial for producing coherent clusters across domains; (ii) SETS algorithm provides an improved text normalisation by using external knowledge; (iv) a method for measuring similarity between documents on a large scale by using many-to-many matching; (v) a semantically enhanced methodology that employs levels of abstraction that correspond to a user's background, understanding and motivation. The achieved results will benefit the research community working in the area of document management, information retrieval, data mining and knowledge management.