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eBook Memory-Based Parsing (Natural Language Processing) ePub

by Sandra Kübler

Sandra Kübler Words Language and Grammar English

eBook Memory-Based Parsing (Natural Language Processing) ePub

Author: Sandra Kübler
Language: English
ISBN: 1588115909
ISBN13: 978-1588115904
Publisher: John Benjamins Publishing Company (October 31, 2004)
Pages: 304
Category: Words Language & Grammar
Subcategory: Reference
Rating: 4.9
Votes: 753
Formats: mobi rtf azw doc
ePub file: 1565 kb
Fb2 file: 1830 kb

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Электронная книга "Memory-Based Parsing", Sandra Kübler.

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3. Memory-Based Parsing. Published by John Benjamins Publishing C. Philadelphia, Pennsylvania, .

Published by John Benjamins Publishing Company, 2004. ISBN 10: 1588115909, ISBN 13: 9781588115904. 3. ISBN 10: 1588115909 ISBN 13: 9781588115904.

Title: Memory-Based Parsing. Author: Sandra Kübler, Eberhard Karls Universität Tübingen. com/cgi-bin/t bookview. Hardback: ISBN: 1588115909 Pages: viii, 294 pp. Price: . Memory-Based Learning (MBL), one of the most influential machine learning paradigms, has been applied with great success to a variety of NLP tasks.

One way to encode the input sequence is via a recurrent neural network (Elman, 1991). Word embeddings are given as inputs to the recur-rent network. For instance, recursive neural networks have been used for parsing (Socher et a. 2011), sentiment analysis (Socher et a. 2013), paraphrase detection (Socher et a. 2011) and question answering (Iyyer et a. 2014) and logical infer-ence (Bowman et a. 2014), among other tasks.

Memory-Based Parsing more. Publication Date: 2005.

LASSA: Emotion Detection via Information Fusion more. and Yu-Yin Hsu. Publication Date: 2012. This paper presents a comparative study of probabilistic treebank parsing of Ger- man, using the Negra and TuBa-D/Z tree- banks more. Memory-Based Parsing more.

Memory Based Parsing book. 1588115909 (ISBN13: 9781588115904).

Memory-Based Learning (MBL), one of the most influential machine learning paradigms, has been applied with great success to a variety of NLP tasks. This monograph describes the application of MBL to robust parsing. Robust parsing using MBL can provide added functionality for key NLP applications, such as Information Retrieval, Information Extraction, and Question Answering, by facilitating more complex syntactic analysis than is currently available. The text presupposes no prior knowledge of MBL. It provides a comprehensive introduction to the framework and goes on to describe and compare applications of MBL to parsing. Since parsing is not easily characterizable as a classification task, adaptations of standard MBL are necessary. These adaptations can either take the form of a cascade of local classifiers or of a holistic approach for selecting a complete tree.The text provides excellent course material on MBL. It is equally relevant for any researcher concerned with symbolic machine learning, Information Retrieval, Information Extraction, and Question Answering.