000			04083nam a22004215i 4500
999			_c387368 _d387368
001			387368
003			ES-MaUEC
005			20230315174652.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2012 sz | s |||| 0|eng d
020			_a9783031021466
024	7		_a10.1007/978-3-031-02146-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP140 _b2012 EB
100	1		_aPiotrowski, Michael _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687391
245	1	0	_aNatural Language Processing for Historical Texts _cby Michael Piotrowski
250			_a1st edition 2012
264		1	_aCham _bSpringer International Publishing _c2012
300			_a1 recurso en línea (XII, 145 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Human Language Technologies _x1947-4059
505	0		_aIntroduction -- 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.
520			_aMore 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.
988			_aSynthesis Collection of Technology_2012
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9683294 _aLingüística histórica
650		7	_2embne _9414657 _aPreservación digital
776	0	8	_iPrinted edition: _z9783031010187
776	0	8	_iPrinted edition: _z9783031032745
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02146-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI