Semantic Relations Between Nominals - Synthesis Lectures on Human Language Technologies (Paperback)
  • Semantic Relations Between Nominals - Synthesis Lectures on Human Language Technologies (Paperback)
zoom

Semantic Relations Between Nominals - Synthesis Lectures on Human Language Technologies (Paperback)

(author), (author), (author)
£40.50
Paperback 119 Pages / Published: 30/04/2013
  • We can order this

Usually dispatched within 2 weeks

  • This item has been added to your basket
People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as ""Opportunity and Curiosity find similar rocks on Mars"" requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation.

The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases. After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction.

Publisher: Morgan & Claypool Publishers
ISBN: 9781608459797
Number of pages: 119
Weight: 218 g
Dimensions: 235 x 187 x 6 mm

You may also be interested in...

Darwin Among the Machines
Added to basket
Machine Learning
Added to basket
£52.99
Mixed media product
Introducing Artificial Intelligence
Added to basket
Emotion: A Very Short Introduction
Added to basket
Machine Learning
Added to basket
£42.50
Paperback
Sentiment Analysis and Opinion Mining
Added to basket
Machine Learning
Added to basket
Understanding Machine Learning
Added to basket
Computer Vision
Added to basket
Analytic Pattern Matching
Added to basket
Theory of Conditional Games
Added to basket
Machine Learning for Hackers
Added to basket
Learning LEGO MINDSTORMS EV3
Added to basket

Reviews

Please sign in to write a review

Your review has been submitted successfully.