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The detailed introduction to computational natural language understanding (NLU) helps the reader to obtain background knowledge necessary to perform practical NLU
The chapter on knowledge base construction shows how to obtain structured knowledge applicable to practical NLU from existing freely available knowledge resources
Includes a new algorithm for debugging inconsistent ontologies and a new methodology for ensuring conceptual consistency
Shows how heterogeneous knowledge can be employed in one single reasoning pipeline
Suggests practical application scenarios of the proposed approach
This book concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). The main objective of the book is to show that inference-based NLU has the potential for practical large scale applications.
First, an introduction to research areas relevant for NLU is given. We review approaches to linguistic meaning, explore knowledge resources, describe semantic parsers, and compare two main forms of inference: deduction and abduction.
In the main part of the book, we propose an integrative knowledge base combining lexical-semantic, ontological, and distributional knowledge. A particular attention is payed to ensuring its consistency. We then design a reasoning procedure able to make use of the large scale knowledge base. We experiment both with a deduction-based NLU system and with an abductive reasoner. For evaluation, we use three different NLU tasks: recognizing textual entailment, semantic role labeling, and interpretation of noun dependencies.