The World Wide Web constitutes the largest existing source of texts written in a great variety of languages. A feasible and sound way of exploiting this data for linguistic research is to compile a static corpus for a given language. There are several adavantages of this approach: (i) Working with such corpora obviates the problems encountered when using Internet search engines in quantitative linguistic research (such as non-transparent ranking algorithms). (ii) Creating a corpus from web data is virtually free. (iii) The size of corpora compiled from the WWW may exceed by several orders of magnitudes the size of language resources offered elsewhere. (iv) The data is locally available to the user, and it can be linguistically post-processed and queried with the tools preferred by her/him. This book addresses the main practical tasks in the creation of web corpora up to giga-token size. Among these tasks are the sampling process (i.e., web crawling) and the usual cleanups including boilerplate removal and removal of duplicated content. Linguistic processing and problems with linguistic processing coming from the different kinds of noise in web corpora are also covered. Finally, the authors show how web corpora can be evaluated and compared to other corpora (such as traditionally compiled corpora).

Human language technology is the study of the methods by which computer programs or electronic devices can analyze, produce, modify or respond to human texts and speech. It consists of natural language processing and computational linguistics on the one hand, and speech technology on the other. This book presents the proceedings of the 9th International Conference, Human Language Technologies – The Baltic Perspective (Baltic HLT 2020), organised in Kaunas, Lithuania on 22 and 23 September 2020. This biennial conference offers researchers a platform to share knowledge on recent advances in human language processing for the Baltic languages, as well as promoting interdisciplinary and international cooperation in human language-technology research within and beyond the Baltic States. In addition to the traditional topics of natural language processing and language technologies, this year’s conference featured a special session on resource and tool development for teaching and learning the less resourced Baltic languages. This year, 42 submissions were received, each of which was evaluated by two reviewers, resulting in a total of 34 papers being accepted for presentation and publication. The book is divided into four sections: speech and text analysis (9 papers); machine translation and natural understanding (6 papers); tools and resources (14 papers); and language learning resources (5 papers). Providing a fascinating overview of current research in the field from a primarily Baltic perspective, the book will be of interest to all those whose work involves human language technology.

A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference

This book contains papers from the Fourth International Conference on Human Language Technologies - the Baltic Perspective (Baltic HLT 2010), held in Riga in October 2010. This conference is the latest in a series which provides a forum for sharing recent advances in human language processing, and promotes cooperation between the computer science and linguistics communities of the Baltic countries and the rest of the world. Bringing together scientists, developers, providers and users, the conference is an opportunity to exchange information, discuss problems, find new synergies, and promote i.

Computational linguistics, speech processing, natural language processing and language technologies in general have all become increasingly important in an era of all-pervading technological development. This book, Human Language Technologies – The Baltic Perspective, presents the proceedings of the 8th International Baltic Human Language Technologies Conference (Baltic HLT 2018), held in Tartu, Estonia, on 27-29 September 2018. The main aim of Baltic HLT is to provide a forum for sharing new ideas and recent advances in computational linguistics and related disciplines, and to promote cooperation between the research communities of the Baltic States and beyond. The 24 articles in this volume cover a wide range of subjects, including machine translation, automatic morphology, text classification, various language resources, and NLP pipelines, as well as speech technology; the latter being the most popular topic with 8 papers. Delivering an overview of the state-of-the-art language technologies from a Baltic perspective, the book will be of interest to all those whose work involves language processing in whatever form.

This book provides system developers and researchers in natural language processing and computational linguistics with the necessary background information for working with the Arabic language. The goal is to introduce Arabic linguistic phenomena and review the state-of-the-art in Arabic processing. The book discusses Arabic script, phonology, orthography, morphology, syntax and semantics, with a final chapter on machine translation issues. The chapter sizes correspond more or less to what is linguistically distinctive about Arabic, with morphology getting the lion's share, followed by Arabic script. No previous knowledge of Arabic is needed. This book is designed for computer scientists and linguists alike. The focus of the book is on Modern Standard Arabic; however, notes on practical issues related to Arabic dialects and languages written in the Arabic script are presented in different chapters. Table of Contents: What is "Arabic"? / Arabic Script / Arabic Phonology and Orthography / Arabic Morphology / Computational Morphology Tasks / Arabic Syntax / A Note on Arabic Semantics / A Note on Arabic and Machine Translation

Human language technologies continue to play an important part in the modern information society.This book contains papers presented at the fifth international conference 'Human Language Technologies - The Baltic Perspective (Baltic HLT 2012)', held in Tartu, Estonia, in October 2012.Baltic HLT provides a special venue for new and ongoing work in computational linguistics and related disciplines, both in the Baltic states and in a broader geographical perspective. It brings together scientists, developers, providers and users of HLT, and is a forum for the sharing of new ideas and recent advances in human language processing, promoting cooperation between the research communities of computer science and linguistics from the Baltic countries and the rest of the world.Twenty long papers, as well as the posters or demos accepted for presentation at the conference, are published here. They cover a wide range of topics: morphological disambiguation, dependency syntax and valency, computational semantics, named entities, dialogue modeling, terminology extraction and management, machine translation, corpus and parallel corpus compiling, speech modeling and multimodal communication. Some of the papers also give a general overview of the state of the art of human language technology and language resources in the Baltic states.This book will be of interest to all those whose work involves the use and application of computational linguistics and related disciplines.

Provides an introduction to natural language processing (NLP) for historical texts and an overview of the state of the art in this field. The book offers overview of methods for the acquisition of historical texts, discusses specific methods, and analyses the relationship between NLP and the digital humanities.

Many NLP tasks have at their core a subtask of extracting the dependencies—who did what to whom—from natural language sentences. This task can be understood as the inverse of the problem solved in different ways by diverse human languages, namely, how to indicate the relationship between different parts of a sentence. Understanding how languages solve the problem can be extremely useful in both feature design and error analysis in the application of machine learning to NLP. Likewise, understanding cross-linguistic variation can be important for the design of MT systems and other multilingual applications. The purpose of this book is to present in a succinct and accessible fashion information about the morphological and syntactic structure of human languages that can be useful in creating more linguistically sophisticated, more language-independent, and thus more successful NLP systems. Table of Contents: Acknowledgments / Introduction/motivation / Morphology: Introduction / Morphophonology / Morphosyntax / Syntax: Introduction / Parts of speech / Heads, arguments, and adjuncts / Argument types and grammatical functions / Mismatches between syntactic position and semantic roles / Resources / Bibliography / Author's Biography / General Index / Index of Languages

In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.