Offers a state-of-the-art overview of computational semantics written by leading researchers in the field
Presents the latest developments in methods and tools for the automatic computation of the content of natural language expressions
Includes discussions of WordNet, VerbNet, semantically annotated corpora and new methodologies for their construction as well as new statistical methods such as the application of distributional semantics
This book is a collection of papers by leading researchers in computational semantics. It presents a state-of-the-art overview of recent and current research in computational semantics, including deillegalscriptions of new methods for constructing and improving resources for semantic computation, such as WordNet, VerbNet, and semantically annotated corpora. It also presents new statistical methods in semantic computation, such as the application of distributional semantics in the compositional calculation of sentence meanings. Computing the meaning of sentences, texts, and spoken or texted dialogue is the ultimate challenge in natural language processing, and the key to a wide range of exciting applications. The breadth and depth of coverage of this book makes it suitable as a reference and overview of the state of the field for researchers in Computational Linguistics, Semantics, Computer Science, Cognitive Science, and Artificial Intelligence.
Computing Meaning: Annotation, Representation, and Inference by Harry Bunt, Johan Bos, and Stephen Pulman . 1 Introduction . 2 About this book . 2.1 Semantic Representation and Compositionality . 2.2 Inference and Understanding . 2.3 Semantic Resources and Annotation . References .- Part I Semantic Representation and Compositionality . Deterministic Statistical Mapping of Sentences to Underspecified Semantics by Hiyan Alshawi, Pi-Chuan Chang, and Michael Ringgaard . 1 Introduction . 2 Direct Semantic Mapping . 3 Semantic Expressions . 3.1 Connectives and Examples . 4 Encoding Semantics as Dependencies . 4.1 Alignment . 4.2 Headedness . 4.3 Label Construction . 5 Experiments . 5.1 Data Preparation . 5.2 Parser . 5.3 Results . 6 Conclusion and Further Work . References .- A formal approach to linking logical form and vector-space lexical semantics by Dan Garrette, Katrin Erk, and Raymond Mooney . 1 Introduction . 2 Background . 3 Linking logical form and vector spaces . 4 Transforming natural language text to logical form . 5 Ambiguity in word meaning . 6 Implicativity . 7 Preliminary Evaluation . 8 Future work . 9 Conclusion . References .- Annotations that effectively contribute to semantic interpretation by Harry Bunt . 1 Introduction: functions of semantic annotations . 2 The semantics of semantic annotations . 2.1 Interpreting annotations expressed in XML . 2.2 The design of semantic annotation languages . 3 Combining semantic annotations and semantic representations . 3.1 Contextualization . 3.2 Semantic alignment . 3.3 Explicitation . 4 Conclusions and perspectives . References .- Concrete Sentence Spaces for Compositional Distributional Models of Meaning by Edward Grefenstette, Mehmoosh Sadrzadeh, Stephen Clark, Bob Coecke, and Stephen Pulman . 1 Introduction . 2 Background . 3 From Truth-Theoretic to Corpus-based Meaning . 4 Concrete Computations . 5 Different Grammatical Structures . 6 Ambiguous Words . 7 Related Work . References .- Part II Inference and Understanding Recognizing Textual Entailment and Computational Semantics by Johan Bos . 1 Introduction . 2 The Logical Method . 2.1 Robust semantic analysis . 2.2 Applying theorem proving . 2.3 Implementation and results . A Critical Evaluation of Performance . 3.1 Proofs found for entailment pairs (true positives) . 3.2 Incorrect proofs found (false positives) . 3.3 Missing proofs (false negatives) . 4 Discussion and Conclusion . References . Abductive Reasoning with a Large Knowledge Base for Discourse Processing by Ekaterina Ovchinnikova, Niloofar Montazeri, Theodore Alexandrov, Jerry R. Hobbs, Michael C. McCord, and Rutu Mulkar-Mehta . 1 Introduction . 2 Weighted Abduction . 3 Discourse Processing Pipeline and Abductive Reasoning . 4 Unification in Weighted Abduction . 5 Knowledge Base . 6 Adapting Mini-TACITUS to a Large Knowledge Base . 6.1 Time and Depth Parameters . 6.2 Filtering out Axioms and Input Propositions . 7 Recognizing Textual Entailment . 8 Experimental Evaluation . 8.1 Weighted Abduction for Recognizing Textual Entailment . 8.2 Semantic Role Labeling . 9 Conclusion and Future Work . References .- Natural logic and natural language inference by Bill MacCartney and Christopher D. Manning . 1 Introduction . 2 An inventory of entailment relations . 3 Joining entailment relations . 4 Lexical entailment relations . 5 Entailment relations and semantic composition . 6 Implicatives and factives . 7 Putting it all together . 8 Implementation and evaluation . 9 Conclusion . References .- Designing Efficient Controlled Languages for Ontologies by Camilo Thorne, Raffaella Bernardi, and Diego Calvanese . 1 Introduction . 2 Controlled Languages and Semantic Complexity . 3 DL-Lite and its Computational Properties . 4 Categorial Grammars . 5 Lite English and its Grammar CG-lite . 5.1 Fragment of Natural Language for DL-Lite . 5.2 Expressing DL-Litecore . 5.3 Expressing DL-LiteR;u . 6 Distribution of Boolean- and non-Boolean-closed Fragments . 7 Related Work . 8 Conclusions . References .- Part III Semantic Resources and Annotation . A Context-Change Semantics for Dialogue Acts by Harry Bunt . 1 Introduction . 2 DiAML: Dialogue Act Markup Language . 2.1 Abstract syntax . 2.2 Concrete Syntax . 2.3 DiAML Semantics . 3 Context Model Structure and Content . 3.1 Types of Context Information . 3.2 Semantic Primitives . 4 Dialogue Act Interpretation . 4.1 The Semantics of Communicative Functions . 4.2 Communicative Function Qualifiers . 5 Conclusion . References .- VerbNet Class Assignment as a WSD Task by Susan Windisch Brown, Dmitriy Dligach and Martha Palmer . 1 Introduction . 2 Related Work . 3 Method . 3.1 The Data . 3.2 Features . 3.3 Experimental Setup . 4 Results . 5 Discussion . 5.1 Contributions of the Features . 5.2 Semlink Annotation . 5.3 Metaphorical Interpetations . 6 Conclusion . 7 Future Work . References .- Annotation of Compositional Operations with GLML by Pustejovsky, Rumshisky, Batiukova, and Moszkowicz . 1 Introduction: Motivation and Previous Work on Semantic . Annotation . 2 Theoretical Preliminaries: Modes of Composition in the Generative Lexicon Theory . 3 Verb-based Annotation. Methodology of Annotation in the Argument Selection and Coercion Task . 3.1 MATTER. 3.2 Task deillegalscription . 3.3 The Type System for Annotation . 3.4 Corpus Development . 3.5 The Data Format . 4 Noun-based Annotation; Exploiting the Qualia . 4.1 Qualia Selection in Modification Constructions . 4.2 Type Selection Involving Dot Objects . 5 Conclusion . References .- Incremental Recognition and Prediction of Dialogue Acts by Volha Petukhova and Harry Bunt . 1 Introduction . 2 Related work . 3 Set-up of classification experiments . 3.1 Tag set . 3.2 Features and data encoding . 3.3 Classifiers and evaluation metrics . 4 Classification results . 4.1 Joint segmentation and classification . 4.2 Fine-grained incremental interpretation: local classification . 4.3 Managing local classifiers: global classification and global search . 5 Conclusions and future research . References . Index
This book is a collection of papers by leading researchers in computational semantics. It presents a state-of-the-art overview of recent and current research in computational semantics, including deillegalscriptions of new methods for constructing and improving resources for semantic computation, such as WordNet, VerbNet, and semantically annotated corpora. It also presents new statistical methods in semantic computation, such as the application of distributional semantics in the compositional calculation of sentence meanings.
Computing the meaning of sentences, texts, and spoken or texted dialogue is the ultimate challenge in natural language processing, and the key to a wide range of exciting applications. The breadth and depth of coverage of this book makes it suitable as a reference and overview of the state of the field for researchers in Computational Linguistics, Semantics, Computer Science, Cognitive Science, and Artificial Intelligence.
Computing Meaning: Annotation, Representation, and Inference by Harry Bunt, Johan Bos, and Stephen Pulman .- Part I Semantic Representation and Compositionality . Deterministic Statistical Mapping of Sentences to Underspecified Semantics by Hiyan Alshawi, Pi-Chuan Chang, and Michael Ringgaard .- A formal approach to linking logical form and vector-space lexical semantics by Dan Garrette, Katrin Erk, and Raymond Mooney .- Annotations that effectively contribute to semantic interpretation by Harry Bunt .- Concrete Sentence Spaces for Compositional Distributional Models of Meaning by Edward Grefenstette, Mehmoosh Sadrzadeh, Stephen Clark, Bob Coecke, and Stephen Pulman .- Part II Inference and Understanding . Recognizing Textual Entailment and Computational Semantics by Johan Bos .- Abductive Reasoning with a Large Knowledge Base for Discourse Processing by Ekaterina Ovchinnikova, Niloofar Montazeri, Theodore Alexandrov, Jerry R. Hobbs, Michael C. McCord, and Rutu Mulkar-Mehta .- Natural logic and natural language inference by Bill MacCartney and Christopher D. Manning .- Designing Efficient Controlled Languages for Ontologies by Camilo Thorne, Raffaella Bernardi, and Diego Calvanese .- Part III Semantic Resources and Annotation . A Context-Change Semantics for Dialogue Acts by Harry Bunt .- VerbNet Class Assignment as a WSD Task by Susan Windisch Brown, Dmitriy Dligach and Martha Palmer .- Annotation of Compositional Operations with GLML by Pustejovsky, Rumshisky, Batiukova, and Moszkowicz .- Incremental Recognition and Prediction of Dialogue Acts by Volha Petukhova and Harry Bunt .- Index .
Inhaltsverzeichnis
Computing Meaning: Annotation, Representation, and Inference by Harry Bunt, Johan Bos, and Stephen Pulman .- Part I Semantic Representation and Compositionality . Deterministic Statistical Mapping of Sentences to Underspecified Semantics by Hiyan Alshawi, Pi-Chuan Chang, and Michael Ringgaard .- A formal approach to linking logical form and vector-space lexical semantics by Dan Garrette, Katrin Erk, and Raymond Mooney .- Annotations that effectively contribute to semantic interpretation by Harry Bunt .- Concrete Sentence Spaces for Compositional Distributional Models of Meaning by Edward Grefenstette, Mehmoosh Sadrzadeh, Stephen Clark, Bob Coecke, and Stephen Pulman .- Part II Inference and Understanding . Recognizing Textual Entailment and Computational Semantics by Johan Bos .- Abductive Reasoning with a Large Knowledge Base for Discourse Processing by Ekaterina Ovchinnikova, Niloofar Montazeri, Theodore Alexandrov, Jerry R. Hobbs, Michael C. McCord, and Rutu Mulkar-Mehta .- Natural logic and natural language inference by Bill MacCartney and Christopher D. Manning .- Designing Efficient Controlled Languages for Ontologies by Camilo Thorne, Raffaella Bernardi, and Diego Calvanese .- Part III Semantic Resources and Annotation . A Context-Change Semantics for Dialogue Acts by Harry Bunt .- VerbNet Class Assignment as a WSD Task by Susan Windisch Brown, Dmitriy Dligach and Martha Palmer .- Annotation of Compositional Operations with GLML by Pustejovsky, Rumshisky, Batiukova, and Moszkowicz .- Incremental Recognition and Prediction of Dialogue Acts by Volha Petukhova and Harry Bunt .- Index .
Klappentext
This book is a collection of papers by leading researchers in computational semantics. It presents a state-of-the-art overview of recent and current research in computational semantics, including deillegalscriptions of new methods for constructing and improving resources for semantic computation, such as WordNet, VerbNet, and semantically annotated corpora. It also presents new statistical methods in semantic computation, such as the application of distributional semantics in the compositional calculation of sentence meanings. Computing the meaning of sentences, texts, and spoken or texted dialogue is the ultimate challenge in natural language processing, and the key to a wide range of exciting applications. The breadth and depth of coverage of this book makes it suitable as a reference and overview of the state of the field for researchers in Computational Linguistics, Semantics, Computer Science, Cognitive Science, and Artificial Intelligence. ¿
Offers a state-of-the-art overview of computational semantics written by leading researchers in the field
Presents the latest developments in methods and tools for the automatic computation of the content of natural language expressions
Includes discussions of WordNet, VerbNet, semantically annotated corpora and new methodologies for their construction as well as new statistical methods such as the application of distributional semantics
Includes supplementary material: sn.pub/extras
