| 000 -LEADER |
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| fixed length control field |
05567nam a22003497a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260630142912.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260630s20242024 a|||er|||| 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9788119523061 [hardbound] |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
University of Cebu-Banilad |
| Transcribing agency |
University of Cebu-Banilad |
| 245 ## - TITLE STATEMENT |
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| Title |
Language processing and knowledge extraction / |
| Statement of responsibility, etc |
edited by Adria Dsilva. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
New Delhi, India : |
| Name of publisher, distributor, etc |
Discovery Publishing House, |
| Date of publication, distribution, etc |
c2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
v, 261 pages : |
| Other physical details |
illustration (black and white) ; |
| Dimensions |
24 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type |
volume |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Contents: Chapter 1 A comparative study to understanding about poetics based on natural — Chapter 2 Real-time static hand gesture recognition for American sign — Chapter 3 DM-L Based feature extraction and classifier ensemble for object recognition — Chapter 4 Let some unforeseen knowledge emerge from heterogeneous documents — Chapter 5 A process for extracting non-taxonomic relationships of ontologies from text — Chapter 6 Word embeddings and semantic spaces in natural language processing — Chapter 7 Effective strategies for language instruction in Physical Education from the perspective of tacit knowledge — Chapter 8 A method of English test knowledge graph construction — Chapter 9 On lemon defect recognition with visual feature extraction and transfers learning — Chapter 10 Text mining to facilitate domain knowledge discovery — Chapter 11 Hippocampal influences on movements: A role in cognitive control? — Chapter 12 Ontogenetic development of neurophysiological mechanisms underlying language processing — Chapter 13 Knowledge extraction from open data repository — Chapter 14 Automated extraction of attributes from natural language attribute-based access control (ABAC) policies — Chapter 15 Seizure classification with selected frequency bands and EEG montages: a natural language processing approach — Chapter 16 Hierarchical and sequential processing of language. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"In the ever-expanding realm of information extraction, the challenges posed by the vast amount of unstructured data have necessitated advancements in techniques and algorithms.<br/>Traditional information extraction systems struggled to cope with the sheer volume and diversity of data, prompting the need for upgrades. Thankfully, recent technological improvements have paved the way for tackling these challenges through the utilization of natural language processing (NLP) techniques.<br/>For several years, the field of NLP has followed the trends of artificial intelligence, relying on algebraic and rule-based approaches. Initially, tasks such as tokenization, segmentation, part-of-speech tagging, and even complex endeavors like machine translation heavily relied on human input to formally describe the tasks at hand. However, the landscape has undergone significant changes in recent years.<br/>The exponential growth of data across various languages and domains, coupled with the evolution of computational power, has propelled the adoption of data-oriented approaches in NLP, primarily driven by machine learning algorithms. Interestingly, the initial goal was not to entirely replace human-based rules with systems relying solely on machine learning.<br/>To illustrate this, let's consider machine translation as an example. Roughly a decade ago, Example-Based Machine Translation gained prominence, employing machine learning to extract segments of texts along with their corresponding translations, essentially building a repository of translation examples. However, a substantial portion of the translation task still relied on rule-based approaches.<br/>In more recent times, with the surge in Deep Learning, machine learning algorithms have fully replaced these rule-based approaches, and not solely for complex tasks like machine translation. Nowadays, almost any task can be tackled using machine learning, provided there is sufficient training data available to develop a robust model.<br/>The book "Language Processing and Knowledge Extraction" serves as a valuable resource, shedding light on the utilization of machine learning approaches in LP, regardless of the task's complexity. Whether treating it as a singular machine learning problem or employing machine learning to address specific components of a task, the book explores the application of machine learning in natural language processing comprehensively.<br/>The advent of machine learning techniques in NLP has revolutionized the field, making it more scalable, adaptable, and capable of handling the ever-increasing volumes of unstructured big data. By harnessing the power of machine learning algorithms, information extraction systems can effectively recognize and summarize extraction issues, enabling efficient processing of large volumes of unstructured data. The book is a must-read for those seeking to explore this exciting field." —Preface |
| 521 ## - TARGET AUDIENCE NOTE |
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| Target audience note |
Adult |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
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| Source of acquisition |
Purchased |
| Deans/Chairperson |
Ortega, Eric |
| Department |
College of Computer Studies |
| Subject Category |
Computer Science |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Natural language processing (Computer science). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Information extraction. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Knowledge representation (Information theory). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Dsilva, Adrian, |
| Relator term |
editor. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Type of record |
Book |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
|---|
| Encoded by |
Janna [new] |
| Date encoded |
06/30/2026 |
