The book focuses on the part of the audio conversation not related to language such as speaking rate (in terms of number of syllables per unit time) and emotion centric features. This text examines using non-linguistics features to infer information from phone calls to call centers. The author analyzes "how" the conversation happens and not "what" the conversation is about by audio signal processing and analysis.

This book constitutes the refereed proceedings of the 5th Language and Technology Conference: Challenges for Computer Science and Linguistics, LTC 2011, held in Poznan, Poland, in November 2011. The 44 revised and in many cases substantially extended papers presented in this volume were carefully reviewed and selected from 111 submissions. The focus of the papers is on the following topics: speech, parsing, computational semantics, text analysis, text annotation, language resources: general issues, language resources: ontologies and Wordnets and machine translation.

This book constitutes the refereed proceedings of the 4th International Workshop on Controlled Natural Language, CNL 2014, held in Galway, Ireland, in August 2014. The 17 full papers and one invited paper presented were carefully reviewed and selected from 26 submissions. The topics include simplified language, plain language, formalized language, processable language, fragments of language, phraseologies, conceptual authoring, language generation, and guided natural language interfaces.

This book constitutes the refereed proceedings of the 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, held in Montpellier, France, in June 2014. The 13 long papers, 8 short papers, 14 poster papers, and 7 demo papers presented together with 2 invited talks in this volume were carefully reviewed and selected from 73 submissions. The papers cover the following topics: syntactic, lexical and semantic analysis; information extraction; information retrieval and sentiment analysis and social networks.

This book constitutes the refereed proceedings of the selected workshops co-located with the 17th International Conference on Theory and Practice of Digital Libraries, TPDL 2013, held in Valletta, Malta, in September 2013. The volume is organized in three parts, containing the 26 revised full papers of the three workshops: Linking and Contextualizing Publications and Datasets (LCPD 2013); Supporting Users Exploration of Digital Libraries (SUEDL 2013); Moving beyond technology: iSchools and education in data curation. Is Data Curator a new role? (DataCur 2013).

Dialect Accent Features for Establishing Speaker Identity: A Case Study discusses the subject of forensic voice identification and speaker profiling. Specifically focusing on speaker profiling and using dialects of the Hindi language, widely used in India, the authors have contributed to the body of research on speaker identification by using accent feature as the discriminating factor. This case study contributes to the understanding of the speaker identification process in a situation where unknown speech samples are in different language/dialect than the recording of a suspect. The authors' data establishes that vowel quality, quantity, intonation and tone of a speaker as compared to Khariboli (standard Hindi) could be the potential features for identification of dialect accent.

Written by leading international experts, this volume presents contributions establishing the feasibility of human language-like communication with robots. The book explores the use of language games for structuring situated dialogues in which contextualized language communication and language acquisition can take place. Within the text are integrated experiments demonstrating the extensive research which targets artificial language evolution. Language Grounding in Robots uses the design layers necessary to create a fully operational communicating robot as a framework for the text, focusing on the following areas: Embodiment; Behavior; Perception and Action; Conceptualization; Language Processing; Whole Systems Experiments. This book serves as an excellent reference for researchers interested in further study of artificial language evolution."

Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers. In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and Maximum A-Posteriori (MAP) to adapt existing phonemic MSA acoustic models with a small amount of dialectal ECA speech data. Speech recognition results indicate a significant increase in recognition accuracy compared to a baseline model trained with only ECA data.

This book constitutes the refereed proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014, held in Honolulu, HI, USA, in June 2014. The 31 revised full papers, 45 short papers and 27 posters presented were carefully viewed and selected from 177 submissions. The specific theme of the ITS 2014 conference is "Creating fertile soil for learning interactions." Besides that, the highly interdisciplinary ITS conferences bring together researchers in computer science, learning sciences, cognitive and educational psychology, sociology, cognitive science, artificial intelligence, machine learning and linguistics. The papers are organized in topical sections on affect; multimodality and metacognition; collaborative learning; data mining and student behavior; dialogue and discourse; generating hints, scaffolds and questions; game-based learning and simulation; graphical representations and learning; student strategies and problem solving; scaling ITS and assessment.

It is becoming crucial to accurately estimate and monitor speech quality in various ambient environments to guarantee high quality speech communication. This practical hands-on book shows speech intelligibility measurement methods so that the readers can start measuring or estimating speech intelligibility of their own system. The book also introduces subjective and objective speech quality measures, and describes in detail speech intelligibility measurement methods. It introduces a diagnostic rhyme test which uses rhyming word-pairs, and includes: An investigation into the effect of word familiarity on speech intelligibility. Speech intelligibility measurement of localized speech in virtual 3-D acoustic space using the rhyme test. Estimation of speech intelligibility using objective measures, including the ITU standard PESQ measures, and automatic speech recognizers.

This book constitutes the refereed proceedings of the 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, held in Montreal, QC, Canada, in May 2014. The 22 regular papers and 18 short papers presented together with 3 invited talks were carefully reviewed and selected from 94 submissions. The papers cover a variety of topics within AI, such as: agent systems; AI applications; automated reasoning; bioinformatics and BioNLP; case-based reasoning; cognitive models; constraint satisfaction; data mining; E-commerce; evolutionary computation; games; information retrieval; knowledge representation; machine learning; multi-media processing; natural language processing; neural nets; planning; privacy-preserving data mining; robotics; search; smart graphics; uncertainty; user modeling; web applications.

This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013, held in Washington, DC, USA, in August 2013. The 14 revised full papers presented were carefully selected during two rounds of reviewing and improvement from numerous original submissions. Intended to give a snapshot of the state-of-the-art research in the field of camera based document analysis and recognition, the papers are organized in topical sections on text detection and recognition in scene images and camera-based systems.

This book concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). The main objective of the book is to show that inference-based NLU has the potential for practical large scale applications. First, an introduction to research areas relevant for NLU is given. We review approaches to linguistic meaning, explore knowledge resources, describe semantic parsers, and compare two main forms of inference: deduction and abduction. In the main part of the book, we propose an integrative knowledge base combining lexical-semantic, ontological, and distributional knowledge. A particular attention is payed to ensuring its consistency. We then design a reasoning procedure able to make use of the large scale knowledge base. We experiment both with a deduction-based NLU system and with an abductive reasoner. For evaluation, we use three different NLU tasks: recognizing textual entailment, semantic role labeling, and interpretation of noun dependencies.

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

It has been estimated that over a billion people are using or learning English as a second or foreign language, and the numbers are growing not only for English but for other languages as well. These language learners provide a burgeoning market for tools that help identify and correct learners' writing errors. Unfortunately, the errors targeted by typical commercial proofreading tools do not include those aspects of a second language that are hardest to learn. This volume describes the types of constructions English language learners find most difficult: constructions containing prepositions, articles, and collocations. It provides an overview of the automated approaches that have been developed to identify and correct these and other classes of learner errors in a number of languages. Error annotation and system evaluation are particularly important topics in grammatical error detection because there are no commonly accepted standards. Chapters in the book describe the options available to researchers, recommend best practices for reporting results, and present annotation and evaluation schemes. The final chapters explore recent innovative work that opens new directions for research. It is the authors' hope that this volume will continue to contribute to the growing interest in grammatical error detection by encouraging researchers to take a closer look at the field and its many challenging problems.

The explosion of information technology has led to substantial growth of web-accessible linguistic data in terms of quantity, diversity and complexity. These resources become even more useful when interlinked with each other to generate network effects.

The general trend of providing data online is thus accompanied by newly developing methodologies to interconnect linguistic data and metadata. This includes linguistic data collections, general-purpose knowledge bases (e.g., the DBpedia, a machine-readable edition of the Wikipedia), and repositories with specific information about languages, linguistic categories and phenomena. The Linked Data paradigm provides a framework for interoperability and access management, and thereby allows to integrate information from such a diverse set of resources.

The contributions assembled in this volume illustrate the band-width of applications of the Linked Data paradigm for representative types of language resources. They cover lexical-semantic resources, annotated corpora, typological databases as well as terminology and metadata repositories. The book includes representative applications from diverse fields, ranging from academic linguistics (e.g., typology and corpus linguistics) over applied linguistics (e.g., lexicography and translation studies) to technical applications (in computational linguistics, Natural Language Processing and information technology).

This volume accompanies the Workshop on Linked Data in Linguistics 2012 (LDL-2012) in Frankfurt/M., Germany, organized by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN). It assembles contributions of the workshop participants and, beyond this, it summarizes initial steps in the formation of a Linked Open Data cloud of linguistic resources, the Linguistic Linked Open Data cloud (LLOD).

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

This white paper is part of a series that promotes knowledge about language technology and its potential. It addresses educators, journalists, politicians, language communities and others. The availability and use of language technology in Europe varies between languages. Consequently, the actions that are required to further support research and development of language technologies also differ for each language. The required actions depend on many factors, such as the complexity of a given language and the size of its community. META-NET, a Network of Excellence funded by the European Commission, has conducted an analysis of current language resources and technologies. This analysis focused on the 23 official European languages as well as other important national and regional languages in Europe. The results of this analysis suggest that there are many significant research gaps for each language. A more detailed expert analysis and assessment of the current situation will help maximise the impact of additional research and minimize any risks. META-NET consists of 54 research centres from 33 countries that are working with stakeholders from commercial businesses, government agencies, industry, research organisations, software companies, technology providers and European universities. Together, they are creating a common technology vision while developing a strategic research agenda that shows how language technology applications can address any research gaps by 2020.

This book constitutes the thoroughly refereed proceedings of the 9th Italian Research Conference on Digital Libraries, held in Rome, Italy, in January/February 2013. The 18 full papers presented together with an invited paper and a panel paper were selected from extended versions of the presentations given at the conference. The papers then went through an additional round of reviewing and revision after the event. The papers are organized in topical sections on information access; Digital Library (DL) architecture; DL projects; semantics and DLs; models and evaluation for DLs; DL applications; discussing DL perspectives.

This book provides extensive insight into the possibilities and challenges of XML in building new information management solutions in networked organizations. After a brief introduction to Web communication features and XML fundamentals, the book examines the benefits of adopting XML and illustrates various types of XML use: XML in document management; XML for data-centric and multimedia components; XML as a format for metadata, including metadata for the Semantic Web; and XML in support of data interchange between software applications and among organizations. The challenges of adopting XML in large-scale information management are also discussed. In addition, applications across a broad spectrum are examined and numerous case studies pertaining to the adoption of XML are presented. The book is particularly suitable for courses offered in Information Studies, Information Systems, or Information Technology. It also serves as an excellent practical guide for professionals in information management and provides important support material for courses in Computer Science and in Business.

The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This book is a unique contribution to that ongoing change. A new methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development - not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.

The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions.

This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing.

Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. The book is intended for students and researchers interested in statistical approaches to Natural Language Processing.

"Slav s work"Coarse-to-Fine Natural Language Processing "represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach."

Eugene Charniak (Brown University)"

We are living in a multilingual world and the diversity in languages which are used to interact with information access systems has generated a wide variety of challenges to be addressed by computer and information scientists. The growing amount of non-English information accessible globally and the increased worldwide exposure of enterprises also necessitates the adaptation of Information Retrieval (IR) methods to new, multilingual settings.

Peters, Braschler and Clough present a comprehensive description of the technologies involved in designing and developing systems for Multilingual Information Retrieval (MLIR). They provide readers with broad coverage of the various issues involved in creating systems to make accessible digitally stored materials regardless of the language(s) they are written in. Details on Cross-Language Information Retrieval (CLIR) are also covered that help readers to understand how to develop retrieval systems that cross language boundaries. Their work is divided into six chapters and accompanies the reader step-by-step through the various stages involved in building, using and evaluating MLIR systems. The book concludes with some examples of recent applications that utilise MLIR technologies. Some of the techniques described have recently started to appear in commercial search systems, while others have the potential to be part of future incarnations.

The book is intended for graduate students, scholars, and practitioners with a basic understanding of classical text retrieval methods. It offers guidelines and information on all aspects that need to be taken into consideration when building MLIR systems, while avoiding too many 'hands-on details' that could rapidly become obsolete. Thus it bridges the gap between the material covered by most of the classical IR textbooks and the novel requirements related to the acquisition and dissemination of information in whatever language it is stored.

Natural language is one of the most important means of human communication. It enables us to express our will, to exchange thoughts and to document our knowledge in written sources. Owing to its substantial role in many facets of human life, technology for automatically analyzing and processing natural language has recently become increasingly important. In fact, natural language processing tools have paved the way for entirely new business opportunities.

The goal of this book is to facilitate the automatic analysis of natural language in process models and to employ this analysis for assisting process model stakeholders. Therefore, a technique is defined that automatically recognizes and annotates process model element labels. In addition, this technique is leveraged to support organizations in effectively utilizing their process models in various ways.

The book is organized into seven chapters. It starts with an overview of business process management and linguistics and continues with conceptual contributions on parsing and annotating process model elements, with the detection and correction of process model guideline violations, with the generation of natural language from process models and finally ends with the derivation of service candidates from process models.

This book constitutes the refereed selected papers from the 14th Chinese Lexical Semantics Workshop, CLSW 2013, held in Zhengzhou, China, in May 2013. The 68 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 153 submissions. They are organized in topical sections covering all major topics of lexical semantics; lexical resources; corpus linguistics and applications on natural language processing.