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Natural Language Processing for Historical Texts by Michael Piotrowski

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Natural Language Processing for Historical Texts

Author : Michael Piotrowski
Publisher : Morgan & Claypool Publishers
Published : 2012-09-01
ISBN-10 : 1608459470
ISBN-13 : 9781608459476
Number of Pages : 157 Pages
Language : en


Descriptions Natural Language Processing for Historical Texts

More and more historical texts are becoming available in digital form. Digitization of paper documents is motivated by the aim of preserving cultural heritage and making it more accessible, both to laypeople and scholars. As digital images cannot be searched for text, digitization projects increasingly strive to create digital text, which can be searched and otherwise automatically processed, in addition to facsimiles. Indeed, the emerging field of digital humanities heavily relies on the availability of digital text for its studies. Together with the increasing availability of historical texts in digital form, there is a growing interest in applying natural language processing (NLP) methods and tools to historical texts. However, the specific linguistic properties of historical texts -- the lack of standardized orthography, in particular -- pose special challenges for NLP. This book aims to give an introduction to NLP for historical texts and an overview of the state of the art in this field. The book starts with an overview of methods for the acquisition of historical texts (scanning and OCR), discusses text encoding and annotation schemes, and presents examples of corpora of historical texts in a variety of languages. The book then discusses specific methods, such as creating part-of-speech taggers for historical languages or handling spelling variation. A final chapter analyzes the relationship between NLP and the digital humanities. Certain recently emerging textual genres, such as SMS, social media, and chat messages, or newsgroup and forum postings share a number of properties with historical texts, for example, nonstandard orthography and grammar, and profuse use of abbreviations. The methods and techniques required for the effective processing of historical texts are thus also of interest for research in other domains. Table of Contents: Introduction / NLP and Digital Humanities / Spelling in Historical Texts / Acquiring Historical Texts / Text Encoding and Annotation Schemes / Handling Spelling Variation / NLP Tools for Historical Languages / Historical Corpora / Conclusion / Bibliography
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Results Natural Language Processing for Historical Texts

Natural language processing for historical texts [electronic resource - Select search scope, currently: catalog all catalog, articles, website, & more in one search; catalog books, media & more in the Stanford Libraries' collections; articles+ journal articles & other e-resources
Natural Language Processing for Historical Texts | SpringerLink - Together with the increasing availability of historical texts in digital form, there is a growing interest in applying natural language processing (NLP) methods and tools to historical texts. However, the specific linguistic properties of historical texts -- the lack of standardized orthography, in particular -- pose special challenges for NLP
Novel Event Detection and Classification for Historical Texts - MIT Press - Abstract. Event processing is an active area of research in the Natural Language Processing community, but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts Particularly in the current era of massive digitization of historical sources: Research in
Natural Language Processing for Historical Texts - ResearchGate - Together with the increasing availability of historical texts in digital form, there is a growing interest in applying natural language processing (NLP) methods and tools to historical texts
Restoring and attributing ancient texts using deep neural networks - Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1
A beginner's guide to natural language processing - Deep learning/deep neural networks have been applied successfully to a variety of problems. You'll find deep learning at the heart of Q&A systems, document summarization, image caption generation, text classification and modeling, and many others. Note that these cases represent natural language understanding and natural language generation
Fundamental Understanding of Text Processing in NLP (Natural Language - A little bit of NLP history. ... This is the field that has made big progress in NLP and its application can be found in concepts like Natural language text processing, summarization, cross
Natural Language Processing for Historical Texts (Synthesis Lectures on - Natural Language Processing for Historical Texts (Synthesis Lectures on Human Language Technologies) [Piotrowski, Michael] on *FREE* shipping on qualifying offers. Natural Language Processing for Historical Texts (Synthesis Lectures on Human Language Technologies)
The History of Natural Language Processing - "The vast quantities of text flooding the World Wide Web have in particular stimulated work on tasks for managing this flood, notably by information extraction and automatic summarizing" (Jones 8). ... Jones, Karen S. "Natural Language Processing: a Historical Review." Artificial Intelligence Review (2001): 1-12. Oct. 2001
Natural language processing | NIST - Historical data from maintenance work orders (MWOs) is a powerful source of information to improve maintenance decisions and procedures. ... Adapting natural language processing for technical text. June 29, 2021. Author(s) Alden A. Dima, Sarah Lukens, Melinda Hodkiewicz, Thurston Sexton, Michael Brundage. Despite recent dramatic successes
Natural language processing - Wikipedia - Natural language processing (NLP) is an interdisciplinary subfield of linguistics, ... The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs. ... Ties with cognitive linguistics are part of the historical heritage of NLP, but they have
The brief history of NLP - Medium - The study of natural language processing generally started in the 1950s, although some work can be found from earlier periods. In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence. Turing test — developed by Alan turing in 1950, is a test of a machine's ability to exhibit
NLP - overview - Stanford University - The field of natural language processing began in the 1940s, after World War II. At this time, people recognized the importance of translation from one language to another and hoped to create a machine that could do this sort of translation automatically. ... Around the same time in history, from 1957-1970, researchers split into two divisions
The Power of Natural Language Processing - Harvard Business Review - The Power of Natural Language Processing. by. Ross Gruetzemacher. April 19, 2022. Westend61/Getty Images. Summary. The conventional wisdom around AI has been that while computers have the edge
PDF Natural Language Processing for Historical Texts - Spelling canonicalization I Natural language is often more complex than that I One problem: relation of modern forms to historical forms is not 1 : n but rather n : m I Example from EModE: weeke 7!ModE 8
Natural Language Processing: A Historical Review | SpringerLink - Abstract. This paper reviews natural language processing (NLP) from the late 1940's to the present, seeking to identify its successive trends as these reflect concerns with different problems or the pursuit of different approaches to solving these problems and building systems as wholes. The review distinguishes four phases in the history of
GPT-1 to GPT-4: Each of OpenAI's GPT Models Explained and Compared - MUO - The use of these diverse datasets allowed GPT-1 to develop strong language modeling abilities. While GPT-1 was a significant achievement in natural language processing (NLP), it had certain limitations. For example, the model was prone to generating repetitive text, especially when given prompts outside the scope of its training data
What is Natural Language Processing? | IBM - Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI —concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics—rule-based modeling of human language
Natural Language Processing for Historical TextsMichael Piotrowski - Natural Language Processing for Historical TextsMichael Piotrowski(Leibniz Institute of European History) Morgan & Claypool ... Chapter 3 ("Spelling in Historical Texts," pp. 11-23) describes the various issues related to spelling variations in historical text. It shows how difficult it may be to deal with both diachronic (, in
PDF Event Extraction from Historical Texts: A New Dataset for Black Rebellions - massive data. Rather, they prefer reading texts and interpreting words in historical and cultural context, or by associating texts with the circumstances sur-rounding their publication. This working methodol-ogy requires an emphasis on the quality of the data over the quantity of the data. Recent advances of natural language processing (NLP
Natural language processing for historical texts - Archive - Together with the increasing availability of historical texts in digital form, there is a growing interest in applying natural language processing (NLP) methods and tools to historical texts. However, the specific linguistic properties of historical texts--the lack of standardized orthography in particular--pose special challenges for NLP
A Natural Language Processing Approach to Understanding ... - ScienceDirect - Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, Association for Computational Linguistics (2017), pp. 2670-2680, 10.18653/v1/D17-1283. ... Newspaper archives + text mining = rich sources of historical geo-spatial data. IOP Conference Series: Earth and Environmental Science, 34 (2016)
A Brief History of Natural Language Processing — Part 1 - Natural language processing (NLP) is a theoretically motivated range of computational techniques for analyzing and representing naturally occurring texts at one or more levels of linguistic
A Brief History of Natural Language Processing (NLP) - Natural Language Processing (NLP) is an aspect of Artificial Intelligence that helps computers understand, interpret, and utilize human languages. NLP allows computers to communicate with people, using a human language. Natural Language Processing also provides computers with the ability to read text, hear speech, and interpret it
Natural Language Processing for Historical Texts by Michael Piotrowski - More and more historical texts are becoming available in digital form. Digitization of paper documents is motivated by the aim of preserving cultural. ... Natural Language Processing for Historical Texts 145. by Michael Piotrowski. Paperback. $44.99
History of natural language processing - Wikipedia - In 1970, William A. Woods introduced the augmented transition network (ATN) to represent natural language input. [4] Instead of phrase structure rules ATNs used an equivalent set of finite state automata that were called recursively. ATNs and their more general format called "generalized ATNs" continued to be used for a number of years
NLP for Historical Texts - Computational linguistics and digital humanities - The Challenge of Historiographical Uncertainty. When people talk about uncertainty in a historical context in digital humanities, most of the time they talk about questions such as the exact date of birth of a person, whether two names refer to one or two persons, what geographical location a place name refers to, or the location of a person at
Deep Learning in Natural Language Processing: History and Achievements - The Evolution of Natural Language Processing (NLP) As we grow, we learn how to use language to communicate with people around us. First, we master our native language: listen to how family members and other children speak and repeat after them; memorize words as they relate to every object and phenomenon; learn sentence structure, punctuation, and other rules of written language
Natural Language Processing for Historical TextsMichael … -
Automatic Language Identification for Celtic Texts | SpringerLink - Web · Language identification is an important Natural Language Processing task. Though, it has been thoroughly researched in the literature, some issues are still open
Building a Text Analysis Pipeline for Classical Languages - Web · Instance-based classification of clinical text is a widely used natural language processing task employed as a step for patient classification, document …
Automatic Language Identification for Celtic Texts - ResearchGate - Web · Language identification is an important Natural Language Processing task. Though, it has been thoroughly researched in the literature, some issues are still open
Natural Language Processing for Historical Texts | SpringerLink -
A Brief History of Natural Language Processing (NLP) -
NLP - overview - Stanford University - WebThe field of natural language processing began in the 1940s, after World War II. At this time, people recognized the importance of translation from one language to another and …
Natural Language Processing for Historical Texts - ResearchGate - WebTogether with the increasing availability of historical texts in digital form, there is a growing interest in applying natural language processing (NLP) methods and tools to historical …
Event Extraction from Historical Texts: A New Dataset for Black … - Webmassive data. Rather, they prefer reading texts and interpreting words in historical and cultural context, or by associating texts with the circumstances sur-rounding their …
History of natural language processing - Wikipedia - The history of machine translation dates back to the seventeenth century, when philosophers such as Leibniz and Descartes put forward proposals for codes which would relate words between languages. All of these proposals remained theoretical, and none resulted in the development of an actual machine. The first patents for "translating machines" were applied for in the mid-1930s. One proposal, by …