Multilingual. Language Processing. Applications. Natural

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1 Multilingual Natural Language Processing Applications

2 Contents Preface xxi Acknowledgments xxv About the Authors xxvii Part I In Theory 1 Chapter 1 Finding the Structure of Words Words and Their Components Tokens Lexemes Morphemes Typology Issues and Challenges Irregularity Ambiguity Productivity Morphological Models Dictionary Lookup Finite-State Morphology Unification-Based Morphology Functional Morphology Morphology Induction Summary 22 Chapter 2 Finding the Structure of Documents Introduction Sentence Boundary Detection Topic Boundary Detection Methods Generative Sequence Classification Methods Discriminative Local Classification Methods 36 xi

3 2.2.3 Discriminative Sequence Classification Methods Hybrid Approaches Extensions for Global Modeling for Sentence Segmentation 2.3 Complexity of the Approaches 2.4 Performances of the Approaches 2.5 Features Features for Both Text and Speech Features Only for Text Features for Speech 2.6 Processing Stages 2.7 Discussion 2.8 Summary Chapter 3 Syntax 3.1 Parsing Natural Language 3.2 Treebanks: A Data-Driven Approach to Syntax 3.3 Representation of Syntactic Structure Syntax Analysis Using Dependency Graphs Syntax Analysis Using Phrase Structure Trees 3.4 Parsing Algorithms Shift-Reduce Parsing Hypergraphs and Chart Parsing Minimum Spanning Trees and Dependency Parsing 3.5 Models for Ambiguity Resolution in Parsing Probabilistic Context-Free Grammars Generative Models for Parsing Discriminative Models for Parsing 3.6 Multilingual Issues-. What Is a Token? 3.7 Summary Tokenization, Case, and Encoding Word Segmentation Morphology Chapter 4 Semantic Parsing 4.1 Introduction 4.2 Semantic Interpretation Structural Ambiguity Word Sense Entity and Event Resolution Predicate-Argument Structure Meaning Representation 4.3 System Paradigms 4.4 Word Sense Resources

4 Contents xiii Systems Software Predicate-Argument Structure Resources Systems Software Meaning Representation Resources Systems Software Summary Word Sense Disambiguation Predicate-Argument Structure Meaning Representation 153 Chapter 5 Language Modeling Introduction n-gram Models Language Model Evaluation Parameter Estimation Maximum-Likelihood Estimation and Smoothing Bayesian Parameter Estimation Large-Scale Language Models Language Model Adaptation Types of Language Models Class-Based Language Models Variable-Length Language Models Discriminative Language Models Syntax-Based Language Models MaxEnt Language Models Factored Language Models Other Tree-Based Language Models Bayesian Topic-Based Language Models Neural Network Language Models Language-Specific Modeling Problems Language Modeling for Morphologically Rich Languages Selection of Subword Units Modeling with Morphological Categories Languages without Word Segmentation Spoken versus Written Languages Multilingual and Crosslingual Language Modeling Multilingual Language Modeling Crosslingual Language Modeling Summary 198

5 xiv Contents Chapter 6 Recognizing Textual Entailment Introduction The Recognizing Textual Entailment Task Problem Definition The Challenge of RTE Evaluating Textual Entailment System Performance Applications of Textual Entailment Solutions RTE in Other Languages A Framework for Recognizing Textual Entailment Case Studies Requirements Analysis Useful Components A General Model Implementation Alignment Inference Training Extracting Discourse Commitments Edit Distance-Based RTE Transformation-Based Approaches Logical Representation and Inference Learning Alignment Independently of Entailment Leveraging Multiple Alignments for RTE Natural Logic Syntactic Tree Kernels Global Similarity Using Limited Dependency Context Latent Alignment Inference for RTE Taking RTE Further Improve Analytics Invent/Tackle New Problems Develop Knowledge Resources Better RTE Evaluation 6.6 Useful Resources Publications Knowledge Resources Natural Language Processing Packages Summary ! Chapter 7 Multilingual Sentiment and Subjectivity Analysis Introduction Definitions Sentiment and Subjectivity Analysis on English Lexicons 262

6 Contents xv Corpora Tools Word- and Phrase-Level Annotations Dictionary-Based Corpus-Based Sentence-Level Annotations Dictionary-Based Corpus-Based Document-Level Annotations Dictionary-Based Corpus-Based What Works, What Doesn't Best Scenario: Manually Annotated Corpora Second Best: Corpus-Based Cross-Lingual Projections Third Best: Bootstrapping a Lexicon Fourth Best: Translating a Lexicon Comparing the Alternatives Summary 277 Part II In Practice 283 Chapter 8 Entity Detection and Tracking Introduction Mention Detection Data-Driven Classification Search for Mentions Mention Detection Features Mention Detection Experiments Coreference Resolution The Construction of Bell Tree Coreference Models: Linking and Starting Model A Maximum Entropy Linking Model Coreference Resolution Experiments Summary 303 Chapter 9 Relations and Events Introduction Relations and Events Types of Relations Relation Extraction as Classification Algorithm Features Classifiers 316

7 xvi 9.5 Other Approaches to Relation Extraction Unsupervised and Semisupervised Approaches Kernel Methods Joint Entity and Relation Detection 9.6 Events 9.7 Event Extraction Approaches 9.8 Moving Beyond the Sentence 9.9 Event Matching 9.10 Future Directions for Event Extraction 9.11 Summary Chapter 10 Machine Translation 10.1 Machine Translation Today 10.2 Machine Translation Evaluation Human Assessment Automatic Evaluation Metrics WER, BLEU, METEOR, Word Alignment Co-occurrence IBM Model Expectation Maximization Alignment Model Symmetrization Word Alignment as Machine Learning Problem 10.4 Phrase-Based Models Model Training Decoding Cube Pruning Log-Linear Models and Parameter Tuning Coping with Model Size 10.5 Tree-Based Models Hierarchical Phrase-Based Models Chart Decoding Syntactic Models 10.6 Linguistic Challenges Lexical Choice Morphology Word Order 10.7 Tools and Data Resources Basic Tools Machine Translation Systems Parallel Corpora

8 Contents xvii 10.8 Future Directions 10.9 Summary Chapter 11 Multilingual Information Retrieval Introduction Document Preprocessing Document Syntax and Encoding Tokenization Normalization Best Practices for Preprocessing Monolingual Information Retrieval Document Representation Index Structures Retrieval Models Query Expansion Document A Priori Models Best Practices for Model Selection CLIR Translation-Based Approaches Machine Translation Interlingual Document Representations Best Practices MLIR Language Identification Index Construction for MLIR Query Translation Aggregation Models Best Practices Evaluation in Information Retrieval Experimental Setup Relevance Assessments Evaluation Measures Established Data Sets Best Practices Tools, Software, and Resources Summary 393 Chapter 12 Multilingual Automatic Summarization Introduction Approaches to Summarization The Classics Graph-Based Approaches Learning How to Summarize Multilingual Summarization 409

9 xviii Contents 12.3 Evaluation Manual Evaluation Methodologies Automated Evaluation Methods Recent Development in Evaluating Summarization Systems Automatic Metrics for Multilingual Summarization 12.4 How to Build a Summarizer Ingredients Devices Instructions 12.5 Competitions and Datasets 12.6 Summary Competitions Data Sets Chapter 13 Question Answering 13.1 Introduction and History 13.2 Architectures 13.3 Source Acquisition and Preprocessing 13.4 Question Analysis 13.5 Search and Candidate Extraction Search over Unstructured Sources Candidate Extraction from Unstructured Sources Candidate Extraction from Structured Sources 13.6 Answer Scoring Overview of Approaches Combining Evidence Extension to List Questions 13.7 Crosslingual Question Answering 13.8 A Case Study 13.9 Evaluation Evaluation Tasks Judging Answer Correctness Performance Metrics Current and Future Challenges Summary and Further Reading Chapter Introduction Distillation 14.2 An Example 14.3 Relevance and Redundancy 14.4 The Rosetta Consortium Distillation System Document and Corpus Preparation Indexing Query Answering

10 Contents xix 14.5 Other Distillation Approaches System Architectures Relevance Redundancy Multimodal Distillation Crosslingual Distillation Evaluation and Metrics Evaluation Metrics in the GALE Program Summary 495 Chapter 15 Spoken Dialog Systems Introduction Spoken Dialog Systems Speech Recognition and Understanding Speech Generation Dialog Manager Voice User Interface Forms of Dialog Natural Language Call Routing Three Generations of Dialog Applications Continuous Improvement Cycle Transcription and Annotation of Utterances Localization of Spoken Dialog Systems Call-Flow Localization Prompt Localization Localization of Grammars The Source Data Training Test Summary 520 Chapter 16 Combining Natural Language Processing Engines Introduction Desired Attributes of Architectures for Aggregating Speech and NLP Engines Flexible, Distributed Componentization Computational Efficiency Data-Manipulation Capabilities Robust Processing Architectures for Aggregation UIMA GATE: General Architecture for Text Engineering InfoSphere Streams 530

11 XX 16.4 Case Studies The GALE Interoperability Demo System Translingual Automated Language Exploitation System (TALES) Real-Time Translation Services (RTTS) 16.5 Lessons Learned Segmentation Involves a Trade-off between Latency and Accuracy Joint Optimization versus Interoperability Data Models Need Usage Conventions Challenges of Performance Evaluation Ripple-Forward Training of Engines 16.6 Summary 16.7 Sample UIMA Code Index

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