The Nave Bayes Model for Unsupervised Word Sense Disambiguation

Name: The Nave Bayes Model for Unsupervised Word Sense Disambiguation
Brand: Springer Nature
SKU: 9783642336935
Availability: InStock

Best Price:
Buy The Nave Bayes Model for Unsupervised Word Sense Disambiguation for $29.99 at @ Link.springer.com
No coupon is required — this is the standard retail price.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $29.99 - $29.99

BEST PRICE

Single Product Purchase

$29.99

@ Link.springer.com BUY Now

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$29.99	$29.99			$29.99		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9783642336935

Condition

New

Product Description

This book presents recent advances (from 2008 to 2012) concerning use of the Nave Bayes model in unsupervised word sense disambiguation (WSD). While WSD, in general, has a number of important applications in various fields of artificial intelligence (information retrieval, text processing, machine translation, message understanding, man-machine communication etc.), unsupervised WSD is considered important because it is language-independent and does not require previously annotated corpora. The Nave Bayes model has been widely used in supervised WSD, but its use in unsupervised WSD has led to more modest disambiguation results and has been less frequent. It seems that the potential of this statistical model with respect to unsupervised WSD continues to remain insufficiently explored. The present book contends that the Nave Bayes model needs to be fed knowledge in order to perform well as a clustering technique for unsupervised WSD and examines three entirely different sources of such knowledge for feature selection: WordNet, dependency relations and web N-grams. WSD with an underlying Nave Bayes model is ultimately positioned on the border between unsupervised and knowledge-based techniques. The benefits of feeding knowledge (of various natures) to a knowledge-lean algorithm for unsupervised WSD that uses the Nave Bayes model as clustering technique are clearly highlighted. The discussion shows that the Nave Bayes model still holds promise for the open problem of unsupervised WSD.

Available Colors

N/A

Available Sizes

N/A