Model Generation for Natural Language Interpretation and Analysis

✍ Scribed by Konrad K.

• Year: 2004
• Tongue: English
• Leaves: 183
• Category: Library

✦ Synopsis

Mathematical theorem proving has undergone an impressive development during the last two decades, resulting in a variety of powerful systems for applications in mathematical deduction and knowledge processing. Natural language processing has become a topic of outstanding relevance in information technology, mainly due to the explosive growth of the Web, where by far the largest part of information is encoded in natural language documents.This monograph focuses on the development of inference tools tailored to applications in natural language processing by demonstrating how the model generation paradigm can be used as a framework for the support of specific tasks in natural language interpretation and natural language based inference in a natural way.The book appears at a pivotal moment, when much attention is being paid to the task of adding a semantic layer to the Web, and representation and processing of natural language based semantic information pops up as a primary requirement for further technological progress.s

✦ Table of Contents

Table of Contents......Page 10
1.1 The Subject of This Volume......Page 16
1.2.1 Interpretation......Page 17
1.2.2 Analysis......Page 18
1.2.3 Computation......Page 19
1.3 Acknowledgments......Page 20
Part I Logics......Page 22
2.2 Preliminaries......Page 24
2.3 Topics......Page 25
2.4 Methods......Page 30
2.5 Related Work......Page 37
3.1 The λ-Calculus in Linguistics......Page 40
3.2 Higher-Order Logic......Page 46
3.3 A Fragment of Higher-Order Logic......Page 53
3.4 Constructing Models......Page 60
4.2 Decidability of Local Minimality......Page 70
Part II Linguistics......Page 72
5.1 Introduction......Page 74
5.2 Some Representations......Page 82
5.3 What We Have Learned so Far......Page 91
6.1 Introduction......Page 94
6.2 Exploring the Meaning of Each Other......Page 95
6.3 Inference to Best Reciprocal Meaning......Page 104
6.4 Experiments......Page 113
6.6 How We Can Understand Each Other......Page 117
7.1 What Is Abduction?......Page 120
7.2 Models for Anaphora Resolution......Page 122
7.3 Weighted Abduction......Page 128
8.1 Introduction......Page 140
8.2 System Architecture......Page 141
8.3 The Syntax......Page 142
8.4 The Semantics......Page 145
8.5 Proof Engines and Controlling Search......Page 152
8.6 System Performance......Page 157
9.1 Why Inference Is Worth the Effort......Page 164
9.2 Contributions......Page 166
9.3 Models as Meaning......Page 167
A.2 Reciprocals: The Boston Pitchers......Page 170
References......Page 174
H......Page 180
W......Page 181