Author: Islam Kamel Ahmed Beltagy
Publisher:
ISBN:
Category :
Languages : en
Pages : 352
Get Book Here
Book Description
With better natural language semantic representations, computers can do more applications more efficiently as a result of better understanding of natural text. However, no single semantic representation at this time fulfills all requirements needed for a satisfactory representation. Logic-based representations like first-order logic capture many of the linguistic phenomena using logical constructs, and they come with standardized inference mechanisms, but standard first-order logic fails to capture the “graded” aspect of meaning in languages. Other approaches for semantics, like distributional models, focus on capturing “graded” semantic similarity of words and phrases but do not capture sentence structure in the same detail as logic-based approaches. However, both aspects of semantics, structure and gradedness, are important for an accurate language semantics representation. In this work, we propose a natural language semantics representation that uses probabilistic logic (PL) to integrate logical with weighted uncertain knowledge. It combines the expressivity and the automated inference of logic with the ability to reason with uncertainty. To demonstrate the effectiveness of our semantic representation, we implement and evaluate it on three tasks, recognizing textual entailment (RTE), semantic textual similarity (STS) and open-domain question answering (QA). These tasks can utilize the strengths of our representation and the integration of logical representation and uncertain knowledge. Our semantic representation 1 has three components, Logical Form, Knowledge Base and Inference, all of which present interesting challenges and we make new contributions in each of them. The first component is the Logical Form, which is the primary meaning representation. We address two points, how to translate input sentences to logical form, and how to adapt the resulting logical form to PL. First, we use Boxer, a CCG-based semantic analysis tool to translate sentences to logical form. We also explore translating dependency trees to logical form. Then, we adapt the logical forms to ensure that universal quantifiers and negations work as expected. The second component is the Knowledge Base which contains “uncertain” background knowledge required for a given problem. We collect the “relevant” lexical information from different linguistic resources, encode them as weighted logical rules, and add them to the knowledge base. We add rules from existing databases, in particular WordNet and the Paraphrase Database (PPDB). Since these are incomplete, we generate additional on-the-fly rules that could be useful. We use alignment techniques to propose rules that are relevant to a particular problem, and explore two alignment methods, one based on Robinson’s resolution and the other based on graph matching. We automatically annotate the proposed rules and use them to learn weights for unseen rules. The third component is Inference. This component is implemented for each task separately. We use the logical form and the knowledge base constructed in the previous two steps to formulate the task as a PL inference problem then develop a PL inference algorithm that is optimized for this particular task. We explore the use of two PL frameworks, Markov Logic Networks (MLNs) and Probabilistic Soft Logic (PSL). We discuss which framework works best for a particular task, and present new inference algorithms for each framework.
Author: R. Zese
Publisher: IOS Press
ISBN: 1614997349
Category : Computers
Languages : en
Pages : 193
Get Book Here
Book Description
The management of uncertainty in the Semantic Web is of foremost importance given the nature and origin of the available data. This book presents a probabilistic semantics for knowledge bases, DISPONTE, which is inspired by the distribution semantics of Probabilistic Logic Programming. The book also describes approaches for inference and learning. In particular, it discusses 3 reasoners and 2 learning algorithms. BUNDLE and TRILL are able to find explanations for queries and compute their probability with regard to DISPONTE KBs while TRILLP compactly represents explanations using a Boolean formula and computes the probability of queries. The system EDGE learns the parameters of axioms of DISPONTE KBs. To reduce the computational cost, EDGEMR performs distributed parameter learning. LEAP learns both the structure and parameters of KBs, with LEAPMR using EDGEMR for reducing the computational cost. The algorithms provide effective techniques for dealing with uncertain KBs and have been widely tested on various datasets and compared with state of the art systems.
Author: Rolf Haenni
Publisher: Springer Science & Business Media
ISBN: 9400700083
Category : Science
Languages : en
Pages : 154
Get Book Here
Book Description
While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.
Author: Rens Bod
Publisher: A Bradford Book
ISBN: 0262025361
Category : Language Arts & Disciplines
Languages : en
Pages : 465
Get Book Here
Book Description
For the past forty years, linguistics has been dominated by the idea that language is categorical and linguistic competence discrete. It has become increasingly clear, however, that many levels of representation, from phonemes to sentence structure, show probabilistic properties, as does the language faculty. Probabilistic linguistics conceptualizes categories as distributions and views knowledge of language not as a minimal set of categorical constraints but as a set of gradient rules that may be characterized by a statistical distribution. Whereas categorical approaches focus on the endpoints of distributions of linguistic phenomena, probabilistic approaches focus on the gradient middle ground. Probabilistic linguistics integrates all the progress made by linguistics thus far with a probabilistic perspective. This book presents a comprehensive introduction to probabilistic approaches to linguistic inquiry. It covers the application of probabilistic techniques to phonology, morphology, semantics, syntax, language acquisition, psycholinguistics, historical linguistics, and sociolinguistics. It also includes a tutorial on elementary probability theory and probabilistic grammars.
Author: Henk Zeevat
Publisher: Springer
ISBN: 3319170643
Category : Language Arts & Disciplines
Languages : en
Pages : 256
Get Book Here
Book Description
The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice’s contributions to pragmatics or in interpretation by abduction.
Author: Rens Bod
Publisher: MIT Press
ISBN: 9780262523387
Category : Language Arts & Disciplines
Languages : en
Pages : 468
Get Book Here
Book Description
For the past forty years, linguistics has been dominated by the idea that language is categorical and linguistic competence discrete. It has become increasingly clear, however, that many levels of representation, from phonemes to sentence structure, show probabilistic properties, as does the language faculty. Probabilistic linguistics conceptualizes categories as distributions and views knowledge of language not as a minimal set of categorical constraints but as a set of gradient rules that may be characterized by a statistical distribution. Whereas categorical approaches focus on the endpoints of distributions of linguistic phenomena, probabilistic approaches focus on the gradient middle ground. Probabilistic linguistics integrates all the progress made by linguistics thus far with a probabilistic perspective. This book presents a comprehensive introduction to probabilistic approaches to linguistic inquiry. It covers the application of probabilistic techniques to phonology, morphology, semantics, syntax, language acquisition, psycholinguistics, historical linguistics, and sociolinguistics. It also includes a tutorial on elementary probability theory and probabilistic grammars.
Author: Shalom Lappin
Publisher: John Wiley & Sons
ISBN: 1119046823
Category : Language Arts & Disciplines
Languages : en
Pages : 771
Get Book Here
Book Description
The second edition of The Handbook of Contemporary Semantic Theory presents a comprehensive introduction to cutting-edge research in contemporary theoretical and computational semantics. Features completely new content from the first edition of The Handbook of Contemporary Semantic Theory Features contributions by leading semanticists, who introduce core areas of contemporary semantic research, while discussing current research Suitable for graduate students for courses in semantic theory and for advanced researchers as an introduction to current theoretical work
Author: Fabrizio Riguzzi
Publisher: CRC Press
ISBN: 1000923215
Category : Computers
Languages : en
Pages : 548
Get Book Here
Book Description
Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols was placed on a sound footing. Probabilistic Answer Set Programming gained a lot of interest together with the studies on the complexity of inference. Algorithms for solving the MPE and MAP tasks are now available. Inference for hybrid programs has changed dramatically with the introduction of Weighted Model Integration. With respect to learning, the first approaches for neuro-symbolic integration have appeared together with algorithms for learning the structure for hybrid programs. Moreover, given the cost of learning PLPs, various works proposed language restrictions to speed up learning and improve its scaling.
Author: Manfred Pinkal
Publisher: Springer Science & Business Media
ISBN: 079233387X
Category : Language Arts & Disciplines
Languages : en
Pages : 404
Get Book Here
Book Description
Semantic underspecification is an essential and pervasive property of natural language. This monograph provides a comprehensive survey of the various phenomena in the field of ambiguity and vagueness. The book discusses the major theories of semantic indefiniteness, which have been proposed in linguistics, philosophy and computer science. It argues for a view of indefiniteness as the potential for further contextual specification, and proposes a unified logical treatment of indefiniteness on this basis. The inherent inconsistency of natural language induced by irreducible imprecision is investigated, and treated in terms of a dynamic extension of the proposed logic. The book is an extended edition of a German monograph and is addressed to advanced students and researchers in theoretical and computational linguistics, logic, philosophy of language, and NL- oriented AI. Although it makes extensive use of logical formalisms, it requires only some basic familiarity with standard predicate logic concepts since all technical terms are carefully explained.
Author: Yuxin Deng
Publisher: Springer
ISBN: 3662451980
Category : Computers
Languages : en
Pages : 258
Get Book Here
Book Description
This book discusses the semantic foundations of concurrent systems with nondeterministic and probabilistic behaviour. Particular attention is given to clarifying the relationship between testing and simulation semantics and characterising bisimulations from metric, logical, and algorithmic perspectives. Besides presenting recent research outcomes in probabilistic concurrency theory, the book exemplifies the use of many mathematical techniques to solve problems in computer science, which is intended to be accessible to postgraduate students in Computer Science and Mathematics. It can also be used by researchers and practitioners either for advanced study or for technical reference.
