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Grigori Sidorov (28)
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Hits 1 – 20 of 29
1
Special Issue: Computational Linguistics and its Applications
Yulia Ledeneva
;
Grigori Sidorov
;
Anton P. Železnikar
In: http://www.informatica.si/PDF/Informatica_2010_1.pdf (2010)
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2
Recent Advances in Computational Linguistics
Yulia Ledeneva
;
Santiago Tianguistenco
;
Grigori Sidorov
In: http://www.informatica.si/PDF/34-1/01_Lendeva%20-%20Recent%20Advances%20in%20Computational%20Linguistics.pdf (2009)
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3
A Bilingual Corpus of Novels Aligned at Paragraph Level
Er Gelbukh
;
Grigori Sidorov
;
José Ángel Vera-félix
In: http://www.cic.ipn.mx/~sidorov/FinTAL2006.pdf (2006)
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4
A Bilingual Corpus of Novels Aligned at Paragraph Level
Er Gelbukh
;
Grigori Sidorov
;
José Ángel Vera-félix
In: http://www.gelbukh.com/CV/Publications/2006/A Bilingual Corpus of Novels Aligned at Paragraph Level.pdf (2006)
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5
Alignment of Paragraphs in Bilingual Texts using Bilingual Dictionaries and Dynamic Programming
Alexander Gelbukh
;
Grigori Sidorov
In: http://www.gelbukh.com/cv/Publications/2006/CIARP-2006-Alignment.pdf (2006)
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6
Morphological Analysis of Inflective Languages Through Generation
Er Gelbukh
;
Grigori Sidorov
In: http://www.gelbukh.com/CV/Publications/2002/SEPLN-2002-Morph.pdf (2002)
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7
Morphological Analysis of Inflective Languages Through Generation
Er Gelbukh
;
Grigori Sidorov
In: http://www.sepln.org/revistaSEPLN/revista/29/29-Pag105.pdf (2002)
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8
Problemas actuales de lingüística computacional
Grigori Sidorov
In: Revista Digital Universitaria (1607 - 6079). Vol. 2, No.1 (2001) (2001)
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9
On Coherence Maintenance in Human-Machine Dialogue with Contextual Ellipses
Er Gelbukh
;
Grigori Sidorov
;
Igor A. Bolshakov
In: http://www.gelbukh.com/CV/Publications/2002/CYS_new1.pdf (1999)
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10
PDF PS www.gelbukh.com
Grigori Sidorov Alexander
;
Dr. Grigori Sidorov
;
Dr. Alexander Gelbukh
In: http://www.gelbukh.com/CV/Publications/1999/ENC-99-Objects.ps (1999)
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11
2Facultad de Ciencias de la Computación
Grigori Sidorov
;
David Pinto
;
Er Gelbukh
In: http://ceur-ws.org/Vol-1180/CLEF2014wn-QA-GomezAdornoEt2014.pdf
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12
Syntactic dependency based n-grams in rule based automatic English as second language grammar correction
Grigori Sidorov
In: http://www.cic.ipn.mx/~sidorov/IJCLA_SN_GRAMS_2013.pdf
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13
Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets
Grigori Sidorov
;
Sabino Mir
;
Francisco Viveros-jiménez
...
In: http://www.gelbukh.com/CV/Publications/2013/Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets.pdf
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14
Lexical-Based Alignment for Reconstruction of Structure in Parallel Texts*
Er Gelbukh
;
Grigori Sidorov
;
Liliana Chanona-hern
In: http://www.gelbukh.com/CV/Publications/2007/Lexical-Based Alignment for Reconstruction of Structure in.pdf
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15
On Similarity of Word Senses in Explanatory Dictionaries
Alexander Gelbukh
;
Grigori Sidorov
;
Yoel Ledo-Mezquita
In: http://www.gelbukh.com/cv/Publications/2003/IJT-2003-senses.pdf
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16
Adapting Cross-Genre Author Profiling to Language and Corpus Notebook for PAN at CLEF 2016
Ilia Markov
;
Helena Gómez-Adorno
;
Grigori Sidorov
...
In: http://ceur-ws.org/Vol-1609/16090947.pdf
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17
Division of Spanish Words into Morphemes with a Genetic Algorithm∗
Er Gelbukh
;
Grigori Sidorov
;
Diego Lara-reyes
...
In: http://www.gelbukh.com/CV/Publications/2008/Division of Spanish Words into Morphemes with a Genetic.pdf
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18
Rule-based System for Automatic Grammar Correction Using Syntactic N-grams for English Language Learning (L2)
Grigori Sidorov
;
Anubhav Gupta
;
Martin Tozer
;
Dolors Catala
;
Angels Catena
;
Rine Fuentes
In: http://aclweb.org/anthology/W/W13/W13-3613.pdf
Abstract:
We describe the system developed for the CoNLL-2013 shared task—automatic English L2 grammar error correction. The system is based on the rule-based approach. It uses very few additional resources: a morphological analyzer and a list of 250 common uncountable nouns, along with the training data provided by the organizers. The system uses the syntactic information available in the training data: this information is represented as syntactic n-grams, i.e. n-grams extracted by following the paths in dependency trees. The system is simple and was developed in a short period of time (1 month). Since it does not employ any additional resources or any sophisticated machine learning methods, it does not achieve high scores (specifically, it has low recall) but could be considered as a baseline system for the task. On the other hand, it shows what can be obtained using a simple rule-based approach and presents a few situations where the rule-based approach can perform better than ML approach. 1
URL:
http://aclweb.org/anthology/W/W13/W13-3613.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.377.1187
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19
Some Linguistic Methods of Improving of the Quality of Document Retrieval on the Internet
Er Gelbukh
;
Grigori Sidorov
;
Yoel Ledo-mezquita
In: http://www.gelbukh.com/CV/Publications/2005/Some Linguistic Methods of Improving the Quality.pdf
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20
Incorporating Linguistic Information to Statistical Word-Level Alignment ⋆
Eduardo Cendejas
;
Grettel Barceló
;
Er Gelbukh
...
In: http://www.gelbukh.com/CV/Publications/2009/Incorporating Linguistic Information to Statistical.pdf
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