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Young Investigators: The Project Approach in the Early Years - Harris Helm Katz
US $1.95
Approximately£1.46
Condition:
“Excellent condition - please see photos”
Like New
A book that has been read, but looks new. The book cover has no visible wear, and the dust jacket (if applicable) is included for hard covers. No missing or damaged pages, no creases or tears, no underlining or highlighting of text, and no writing in the margins. May have no identifying marks on the inside cover. No wear and tear. See the seller’s listing for full details and description of any imperfections.
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Collection:
Free collection in person from Newcomerstown, Ohio, United States
Postage:
US $5.38 (approx £4.03) USPS Media MailTM.
Located in: Newcomerstown, Ohio, United States
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Estimated between Tue, 29 Apr and Fri, 2 May to 43230
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eBay item number:134007241648
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Item specifics
- Condition
- Like New
- Seller notes
- “Excellent condition - please see photos”
- Subject Area
- Children & Family
- Book Title
- Young Investigators: The Project Approach in the Early Years
- Custom Bundle
- No
- Subject
- Education
- Series
- Early Childhood Education Series
- ISBN-10
- 0807740160
- Educational Level
- Adult & Further Education
- Personalized
- No
- Level
- Intermediate, Advanced
- Features
- Illustrated
- Country/Region of Manufacture
- United States
- ISBN
- 9780807740163
About this product
Product Identifiers
Publisher
Teacher's College Press
ISBN-10
0807740160
ISBN-13
9780807740163
eBay Product ID (ePID)
1833970
Product Key Features
Number of Pages
176 Pages
Publication Name
Young Investigators : the Project Approach in the Early Years
Language
English
Subject
Early Childhood (Incl. Preschool & Kindergarten)
Publication Year
2000
Type
Textbook
Subject Area
Education
Format
Trade Paperback
Dimensions
Item Height
0.3 in
Item Weight
15.5 Oz
Item Length
10.9 in
Item Width
8.5 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
00-057741
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
372.21
Synopsis
A new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a number of results are produced to address the IETS problem in an unsupervised fashion. In particular, the authors develop, implement and evaluate distinct IETS methods, namely "ONDUX," "JUDIE" and "iForm.""ONDUX" (On Demand Unsupervised Information Extraction) is an unsupervised probabilistic approach for IETS that relies on content-based features to bootstrap the learning of structure-based features. "JUDIE" (Joint Unsupervised Structure Discovery and Information Extraction) aims at automatically extracting several semi-structured data records in the form of continuous text and having no explicit delimiters between them. In comparison with other IETS methods, including "ONDUX," "JUDIE" faces a task considerably harder that is, extracting information while simultaneously uncovering the underlying structure of the implicit records containing it." iForm" applies the authors approach to the task of Web form filling. It aims at extracting segments from a data-rich text given as input and associating these segments with fields from a target Web form.All of these methods were evaluated considering different experimental datasets, which are used to perform a large set of experiments in order to validate the presented approach and methods. These experiments indicate that the proposed approach yields high quality results when compared to state-of-the-art approaches and that it is able to properly support IETS methods in a number of real applications. The findings will prove valuable to practitioners in helping them to understand the current state-of-the-art in unsupervised information extraction techniques, as well as to graduate and undergraduate students of web data management., This work aims to illustrate how children - even those considered "at-risk", may benefit from the exploratory and child-initiated nature of project investigations in order to achieve mastery of basic literacy skills.
LC Classification Number
LB1139.35.P8H46 2001
Item description from the seller
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