http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Synthesis digital library of engineering and computer science
1 online resource (xii, 107 pages) : illustrations
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.
1. Introduction -- 1.1 Motivation -- 1.2 Applications -- 1.3 What this book is about -- 1.4 What this book is not about --
2. Relations between nominals, relations between concepts -- 2.1 Historical overview -- 2.2 A menagerie of relation schemata -- 2.2.1 Relations between words, relations within noun compounds -- 2.2.2 Relations in ontologies and knowledge bases -- 2.2.3 A conclusion -- 2.3 Dimensions of variation across relations -- 2.3.1 Properties of relations -- 2.3.2 Properties of relation schemata -- 2.4 Summary --
3. Extracting semantic relations with supervision -- 3.1 Data -- 3.1.1 MUC and ACE -- 3.1.2 SemEval-2007/2010 -- 3.1.3 Datasets for relations in noun-noun compounds -- 3.1.4 Relations in manually built ontologies -- 3.1.5 Relations from collaboratively built resources -- 3.1.6 Domain-specific data -- 3.2 Features -- 3.2.1 Entity features -- 3.2.2 Relational features -- 3.3 Learning methods -- 3.3.1 Supervised machine learning -- 3.3.2 Learning algorithms -- 3.3.3 Determining the semantic class of relation arguments -- 3.4 Beyond binary relations -- 3.5 Practical considerations -- 3.6 Summary --
4. Extracting semantic relations with little or no supervision -- 4.1 Mining ontologies from machine-readable dictionaries -- 4.2 Mining relations with patterns -- 4.2.1 Bootstrapping relations from large corpora -- 4.2.2 Tackling semantic drift -- 4.3 Distant supervision -- 4.4 Unsupervised relation extraction -- 4.4.1 Extracting IS-A relations -- 4.4.2 Emergent relations in open relation extraction -- 4.4.3 Extreme unsupervised relation extraction -- 4.5 Self-supervised relation extraction -- 4.6 Web-scale relation extraction -- 4.6.1 Never-ending language learner -- 4.6.2 Machine reading at the University of Washington -- 4.7 Summary --
5. Conclusion -- Bibliography -- Authors' biographies -- Index.
Part of: Synthesis digital library of engineering and computer science.
Title from PDF title page (viewed on May 20, 2013).
Includes bibliographical references (pages 85-102) and index.
LANGUAGE ARTS & DISCIPLINES / Linguistics / Historical & Comparative
Synthesis lectures on human language technologies ; # 19