Ontology Based Interpretation Of Natural Language

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Ontology-Based Interpretation of Natural Language

Philipp Cimiano
Ontology Based Interpretation of Natural Language Book PDF

Author : Philipp Cimiano
Publisher : Springer Nature
Page : 158 pages
File Size : 43,7 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031021541

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Ontology-Based Interpretation of Natural Language by Philipp Cimiano Pdf

For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. The main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionally construct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Ontologies / Linguistic Formalisms / Ontology Lexica / Grammar Generation / Putting Everything Together / Ontological Reasoning for Ambiguity Resolution / Temporal Interpretation / Ontology-Based Interpretation for Question Answering / Conclusion / Bibliography / Authors' Biographies

Ontology-Based Interpretation of Natural Language

Philipp Cimiano,Christina Unger,John McCrae
Ontology Based Interpretation of Natural Language Book PDF

Author : Philipp Cimiano,Christina Unger,John McCrae
Publisher : Unknown
Page : 0 pages
File Size : 42,9 Mb
Release : 2014
Category : Computational linguistics
ISBN : 1608459896

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Ontology-Based Interpretation of Natural Language by Philipp Cimiano,Christina Unger,John McCrae Pdf

For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. The main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionally construct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web.

Semantic Similarity from Natural Language and Ontology Analysis

Sébastien Harispe
Semantic Similarity from Natural Language and Ontology Analysis Book PDF

Author : Sébastien Harispe
Publisher : Springer Nature
Page : 245 pages
File Size : 54,5 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031021565

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Semantic Similarity from Natural Language and Ontology Analysis by Sébastien Harispe Pdf

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning---intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.

The Semantic Web

Karl Aberer
The Semantic Web Book PDF

Author : Karl Aberer
Publisher : Springer Science & Business Media
Page : 998 pages
File Size : 46,6 Mb
Release : 2007-10-22
Category : Business & Economics
ISBN : 9783540762973

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The Semantic Web by Karl Aberer Pdf

This book constitutes the refereed proceedings of the joint 6th International Semantic Web Conference, ISWC 2007, and the 2nd Asian Semantic Web Conference, ASWC 2007, held in Busan, Korea, in November 2007. The 50 revised full academic papers and 12 revised application papers presented together with 5 Semantic Web Challenge papers and 12 selected doctoral consortium articles were carefully reviewed and selected from a total of 257 submitted papers to the academic track and 29 to the applications track. The papers address all current issues in the field of the semantic Web, ranging from theoretical and foundational aspects to various applied topics such as management of semantic Web data, ontologies, semantic Web architecture, social semantic Web, as well as applications of the semantic Web. Short descriptions of the top five winning applications submitted to the Semantic Web Challenge competition conclude the volume.

Computational Linguistics and Intelligent Text Processing

Alexander Gelbukh
Computational Linguistics and Intelligent Text Processing Book PDF

Author : Alexander Gelbukh
Publisher : Springer Science & Business Media
Page : 778 pages
File Size : 45,7 Mb
Release : 2010-03-18
Category : Computers
ISBN : 9783642121159

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Computational Linguistics and Intelligent Text Processing by Alexander Gelbukh Pdf

This book constitutes the proceedings of the 11th International Conference on Computational Linguistics and Intelligent Text Processing, held in Iaşi, Romania, in March 2010. The 60 paper included in the volume were carefully reviewed and selected from numerous submissions. The book also includes 3 invited papers. The topics covered are: lexical resources, syntax and parsing, word sense disambiguation and named entity recognition, semantics and dialog, humor and emotions, machine translation and multilingualism, information extraction, information retrieval, text categorization and classification, plagiarism detection, text summarization, and speech generation.

Ontologies and Adaptivity in Dialogue for Question Answering

D. Sonntag
Ontologies and Adaptivity in Dialogue for Question Answering Book PDF

Author : D. Sonntag
Publisher : IOS Press
Page : 422 pages
File Size : 46,9 Mb
Release : 2010-01-05
Category : Computers
ISBN : 9781614993384

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Ontologies and Adaptivity in Dialogue for Question Answering by D. Sonntag Pdf

Question answering (QA) has become one of the fastest growing topics in computational linguistics and information access. To advance research in the area of dialogue-based question answering, we propose a combination of methods from different scientific fields (i.e., Information Retrieval, Dialogue Systems, Semantic Web, and Machine Learning). This book sheds light on adaptable dialogue-based question answering. We demonstrate the technical and computational feasibility of the proposed ideas, the introspective methods in particular, by beginning with an extensive introduction to the dialogical problem domain which motivates the technical implementation. The ideas have been carried out in a mature natural language processing (NLP) system, the SmartWeb dialogue system, which was developed between 2004 and 2007 by partners from academia and industry. We have attempted to make this book a self-containing text and provide an extra section on the interdisciplinary scientific background. The target audience for this book comprises of researchers and students interested in the application potential of semantic technologies for difficult AI tasks such as working dialogue and QA systems.

Building and Evaluating Domain Ontologies

Gintarė Grigonytė
Building and Evaluating Domain Ontologies Book PDF

Author : Gintarė Grigonytė
Publisher : Logos Verlag Berlin GmbH
Page : 213 pages
File Size : 55,9 Mb
Release : 2010
Category : Computers
ISBN : 9783832526573

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Building and Evaluating Domain Ontologies by Gintarė Grigonytė Pdf

An ontology is a knowledge representation structure made up of concepts and their interrelations. It represents shared understanding delineated by some domain. The building of an ontology can be addressed from the perspective of natural language processing. This thesis discusses the validity and theoretical background of knowledge acquisition from natural language. It also presents the theoretical and experimental framework for NLP-driven ontology building and evaluation tasks.

Bayesian Analysis in Natural Language Processing

Shay Cohen
Bayesian Analysis in Natural Language Processing Book PDF

Author : Shay Cohen
Publisher : Springer Nature
Page : 266 pages
File Size : 48,9 Mb
Release : 2022-11-10
Category : Computers
ISBN : 9783031021619

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Bayesian Analysis in Natural Language Processing by Shay Cohen Pdf

Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. We cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.

Ontology and the Lexicon

Chu-ren Huang
Ontology and the Lexicon Book PDF

Author : Chu-ren Huang
Publisher : Cambridge University Press
Page : 361 pages
File Size : 41,8 Mb
Release : 2010-04
Category : Computers
ISBN : 9780521886598

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Ontology and the Lexicon by Chu-ren Huang Pdf

An edited collection focusing on the technology involved in enabling integration between lexical resources and semantic technologies.

Linguistic Fundamentals for Natural Language Processing II

Emily M. Bender
Linguistic Fundamentals for Natural Language Processing II Book PDF

Author : Emily M. Bender
Publisher : Springer Nature
Page : 250 pages
File Size : 41,8 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031021725

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Linguistic Fundamentals for Natural Language Processing II by Emily M. Bender Pdf

Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG). This is because the aims of these fields are to build systems that understand what people mean when they speak or write, and that can produce linguistic strings that successfully express to people the intended content. In order for NLP to scale beyond partial, task-specific solutions, researchers in these fields must be informed by what is known about how humans use language to express and understand communicative intents. The purpose of this book is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that's accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics.

Modern Computational Models of Semantic Discovery in Natural Language

Žižka, Jan
Modern Computational Models of Semantic Discovery in Natural Language Book PDF

Author : Žižka, Jan
Publisher : IGI Global
Page : 335 pages
File Size : 44,7 Mb
Release : 2015-07-17
Category : Computers
ISBN : 9781466686915

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Modern Computational Models of Semantic Discovery in Natural Language by Žižka, Jan Pdf

Language—that is, oral or written content that references abstract concepts in subtle ways—is what sets us apart as a species, and in an age defined by such content, language has become both the fuel and the currency of our modern information society. This has posed a vexing new challenge for linguists and engineers working in the field of language-processing: how do we parse and process not just language itself, but language in vast, overwhelming quantities? Modern Computational Models of Semantic Discovery in Natural Language compiles and reviews the most prominent linguistic theories into a single source that serves as an essential reference for future solutions to one of the most important challenges of our age. This comprehensive publication benefits an audience of students and professionals, researchers, and practitioners of linguistics and language discovery. This book includes a comprehensive range of topics and chapters covering digital media, social interaction in online environments, text and data mining, language processing and translation, and contextual documentation, among others.

Embeddings in Natural Language Processing

Mohammad Taher Pilehvar,Jose Camacho-Collados
Embeddings in Natural Language Processing Book PDF

Author : Mohammad Taher Pilehvar,Jose Camacho-Collados
Publisher : Morgan & Claypool Publishers
Page : 177 pages
File Size : 55,5 Mb
Release : 2020-11-13
Category : Computers
ISBN : 9781636390222

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Embeddings in Natural Language Processing by Mohammad Taher Pilehvar,Jose Camacho-Collados Pdf

Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.

New Trends of Research in Ontologies and Lexical Resources

Alessandro Oltramari,Piek Vossen,Lu Qin,Eduard Hovy
New Trends of Research in Ontologies and Lexical Resources Book PDF

Author : Alessandro Oltramari,Piek Vossen,Lu Qin,Eduard Hovy
Publisher : Springer Science & Business Media
Page : 289 pages
File Size : 41,6 Mb
Release : 2013-02-01
Category : Computers
ISBN : 9783642317828

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New Trends of Research in Ontologies and Lexical Resources by Alessandro Oltramari,Piek Vossen,Lu Qin,Eduard Hovy Pdf

In order to exchange knowledge, humans need to share a common lexicon of words as well as to access the world models underlying that lexicon. What is a natural process for a human turns out to be an extremely hard task for a machine: computers can’t represent knowledge as effectively as humans do, which hampers, for example, meaning disambiguation and communication. Applied ontologies and NLP have been developed to face these challenges. Integrating ontologies with (possibly multilingual) lexical resources is an essential requirement to make human language understandable by machines, and also to enable interoperability and computability across information systems and, ultimately, in the Web. This book explores recent advances in the integration of ontologies and lexical resources, including questions such as building the required infrastructure (e.g., the Semantic Web) and different formalisms, methods and platforms for eliciting, analyzing and encoding knowledge contents (e.g., multimedia, emotions, events, etc.). The contributors look towards next-generation technologies, shifting the focus from the state of the art to the future of Ontologies and Lexical Resources. This work will be of interest to research scientists, graduate students, and professionals in the fields of knowledge engineering, computational linguistics, and semantic technologies.

Statistical Methods for Annotation Analysis

Silviu Paun,Ron Artstein,Massimo Poesio
Statistical Methods for Annotation Analysis Book PDF

Author : Silviu Paun,Ron Artstein,Massimo Poesio
Publisher : Morgan & Claypool Publishers
Page : 218 pages
File Size : 53,7 Mb
Release : 2022-01-13
Category : Computers
ISBN : 9781636392547

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Statistical Methods for Annotation Analysis by Silviu Paun,Ron Artstein,Massimo Poesio Pdf

Labelling data is one of the most fundamental activities in science, and has underpinned practice, particularly in medicine, for decades, as well as research in corpus linguistics since at least the development of the Brown corpus. With the shift towards Machine Learning in Artificial Intelligence (AI), the creation of datasets to be used for training and evaluating AI systems, also known in AI as corpora, has become a central activity in the field as well. Early AI datasets were created on an ad-hoc basis to tackle specific problems. As larger and more reusable datasets were created, requiring greater investment, the need for a more systematic approach to dataset creation arose to ensure increased quality. A range of statistical methods were adopted, often but not exclusively from the medical sciences, to ensure that the labels used were not subjective, or to choose among different labels provided by the coders. A wide variety of such methods is now in regular use. This book is meant to provide a survey of the most widely used among these statistical methods supporting annotation practice. As far as the authors know, this is the first book attempting to cover the two families of methods in wider use. The first family of methods is concerned with the development of labelling schemes and, in particular, ensuring that such schemes are such that sufficient agreement can be observed among the coders. The second family includes methods developed to analyze the output of coders once the scheme has been agreed upon, particularly although not exclusively to identify the most likely label for an item among those provided by the coders. The focus of this book is primarily on Natural Language Processing, the area of AI devoted to the development of models of language interpretation and production, but many if not most of the methods discussed here are also applicable to other areas of AI, or indeed, to other areas of Data Science.

Neural Network Methods for Natural Language Processing

Yoav Goldberg
Neural Network Methods for Natural Language Processing Book PDF

Author : Yoav Goldberg
Publisher : Springer Nature
Page : 20 pages
File Size : 50,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031021657

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Neural Network Methods for Natural Language Processing by Yoav Goldberg Pdf

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.