000 03612nam a22004935i 4500
001 978-3-642-25923-4
003 DE-He213
005 20140220083307.0
007 cr nn 008mamaa
008 111207s2012 gw | s |||| 0|eng d
020 _a9783642259234
_9978-3-642-25923-4
024 7 _a10.1007/978-3-642-25923-4
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aBiemann, Chris.
_eauthor.
245 1 0 _aStructure Discovery in Natural Language
_h[electronic resource] /
_cby Chris Biemann.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2012.
300 _aXX, 178p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTheory and Applications of Natural Language Processing
505 0 _aForeword by Antal van den Bosch -- 1.Introduction -- 2.Graph Models -- 3.SmallWorlds of Natural Language -- 4.Graph Clustering -- 5.Unsupervised Language Separation -- 6.Unsupervised Part-of-Speech Tagging -- 7.Word Sense Induction and Disambiguation -- 8.Conclusion -- References .
520 _aCurrent language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet. This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process? After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction. The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems.  
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aComputational linguistics.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Linguistics.
650 2 4 _aGraph Theory.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642259227
830 0 _aTheory and Applications of Natural Language Processing
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-25923-4
912 _aZDB-2-SCS
999 _c102505
_d102505