Free Graph-based Natural Language Processing And Information Retrieval Ebooks Online

                
              
Free 
Graph-based Natural Language Processing And Information Retrieval Ebooks Online
Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.
Hardcover: 202 pages
Publisher: Cambridge University Press; 1 edition (April 11, 2011)
Language: English
ISBN-10: 0521896134
ISBN-13: 978-0521896139
Product Dimensions:  6.1 x 0.5 x 9.2 inches
Shipping Weight: 15.5 ounces (View shipping rates and policies)
Average Customer Review:  3.3 out of 5 stars  See all reviews (3 customer reviews)
Best Sellers Rank: #1,379,693 in Books (See Top 100 in Books)   #108 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Natural Language Processing   #559 in Books > Computers & Technology > Computer Science > Human-Computer Interaction   #17760 in Books > Textbooks > Computer Science
I have bought this book right when I stepped into Text mining (NLP) area for my current job as Data Scientist. For many months the book accrued dust on my shelf, it was until recently that I read almost half of the book in two sittings. Here is why I suddenly found this book very useful:I was cooking up some ideas on using a graph based approach to model the problem. I spent many hours in thinking on this problem. Eventually I realized, I need a well articulated body of knowledge to refine my thinking, give words to ideas, reference other people's similar work, and avoid reinventing the wheel. This book did all of the above, and more that I am omitting because of space, and that not yet realized.There is a subtle point here, I will spell out for your convenience, this is for you if you are ready. In addition, it does not go in excess detail that you lose track of your thinking, at the same time it refers enough examples from literature on every topic that you can pursue further to find more details; therefore it's a strong reference book and may not be a textbook.My rating is 4 out of 5:1) I do not see this as a textbook.2) The scope is very specialized.3) You have to have a minimum level of exposure to graph algorithms, linear algebra, and probability to really benefit from the book.4) A self-contained appendix to refresh above mentioned topics may improve acceptance of this book, and hence the rating.I hope this helps the future reader.
This book covers lots of topics (as you see can from its TOC) but does not provide sound explanation, intuition, or theory. I would have given a one star rating but my two star rating reflects the fact that you get a list of topics at one place that you can use to further explore.
While this book provides a good background on NLP processing wherein the linguistic entities are individually represented by nodes (and/or edges) in a graph, the title misled me a bit since there is no discussion of theoretical approaches where each linguistic entity is represented by a directed graph (i.e. typed feature structures, Carpenter 1992, etc.) and the operations (i.e. graph unification) are defined over these complex structures. This being my area of interest--and what I was looking for when purchasing the book--I thought I'd mention that this book does not cover the topic.
Graph-based Natural Language Processing and Information Retrieval Deep Learning: Natural Language Processing in Python with Word2Vec: Word2Vec and Word Embeddings in Python and Theano (Deep Learning and Natural Language Processing Book 1) Deep Learning: Natural Language Processing in Python with GLoVe: From Word2Vec to GLoVe in Python and Theano (Deep Learning and Natural Language Processing) Deep Learning: Natural Language Processing in Python with Recursive Neural Networks: Recursive Neural (Tensor) Networks in Theano (Deep Learning and Natural Language Processing Book 3) Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition graph paper composition book: Blue Damask Design,Graph Paper Notebook and Conversion Chart, 7.5 x 9.25, 160 Pages For for School / Teacher / Office / Student Composition Book Information Processing with Evolutionary Algorithms: From Industrial Applications to Academic Speculations (Advanced Information and Knowledge Processing) Information Representation and Retrieval in the Digital Age, Second Edition Introduction to Modern Information Retrieval Natural Language Processing for Social Media (Synthesis Lectures on Human Language Technologies) Natural Gas Trading: From Natural Gas Stocks to Natural Gas Futures- Your Complete, Step-by-Step Guide to Natural Gas Trading Prolog and Natural-Language Analysis (Center for the Study of Language & Information - Lecture Notes) In the Beginning...': A Catholic Understanding of the Story of Creation and the Fall (Ressourcement: Retrieval and Renewal in Catholic Thought (RRRCT)) Natural Language Processing with Java and LingPipe Cookbook The True Source of Healing: How the Ancient Tibetan Practice of Soul Retrieval Can Transform and Enrich Your Life Natural Language Processing in Lisp: An Introduction to Computational Linguistics Functional Grammar in PROLOG (Natural Language Processing) Mastering Natural Language Processing with Python Natural Language Processing Contemporary Drug Information: An Evidence-Based Approach (Gaenelein, Contemporary Drug Information) 