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Hits 21 – 32 of 32
21
c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Learning Translation Templates from Bilingual Translation Examples
Ilyas Cicekli
;
H. Altay Güvenir
In: http://www.cs.bilkent.edu.tr/~guvenir/publications/AI-15-1-57.pdf
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22
Generation of Turkish Verbal Groups with Systemic-functional Grammar
Turgay Korkmaz
;
Ilyas Cicekli
In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/tainn96.pdf
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23
Generation of Simple Turkish Sentences with Systemic-Functional Grammar
Ilyas Cicekli Dept
;
Ilyas Cicekli
;
Turgay Korkmaz
In: http://lcg-www.uia.ac.be/conll98/ps/165173ci.ps
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24
Lexical Cohesion Based Topic Modeling for Summarization
Gonenc Ercan
;
Ilyas Cicekli
In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/cicling2007.pdf
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25
Using Lexical Chains for Keyword Extraction
Gonenc Ercan
;
Ilyas Cicekli
In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/ipm2007.pdf
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26
A Link Grammar for an Agglutinative Language
Ozlem Istek
;
Ilyas Cicekli
In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/ranlp2007_linkgrammer.pdf
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27
Ordering Translation Templates by Assigning Confidence Factors
Zeynep Öz
;
Ilyas Cicekli
In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/amta98-confidencefactor.pdf
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28
An Ontology-Based Approach to Parsing Turkish Sentences?
Murat Temizsoy
;
Ilyas Cicekli
In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/amta98-ontologyparsing.pdf
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29
Automatic Creation of a Morphological Processor in Logic Programming Environment 1
Ilyas Cicekli
;
Murat Temizsoy
In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/pap97.pdf
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30
Generic Text Summarization for Turkish
Celal Cığır
;
Mücahid Kutlu
;
Ilyas Cicekli
In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/iscis2009Summarization.pdf
Abstract:
Abstract — In this paper, we propose a generic text summarization method that generates summaries of Turkish texts by ranking sentences according to their scores calculated using their surface level features and extracting the highest ranked ones from the original documents. In order to extract sentences which form a summary with an extensive coverage of main content of the text and less redundancy, we use the features such as term frequency, key phrase, centrality, title similarity and position of the sentence in the original text. Sentence rank is computed using a score function that uses its feature values and the weights of the features. The best feature weights are learned using machine learning techniques with the help of human constructed summaries. Performance evaluation is conducted by comparing summarization outputs with manual summaries generated by 25 independent human evaluators. This paper presents one of the first Turkish summarization systems, and its results are promising.
Keyword:
Natural Language Processing
;
Summary Extraction
;
Text Summarization
URL:
http://www.cs.bilkent.edu.tr/~ilyas/PDF/iscis2009Summarization.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.5280
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31
TURKSENT: A Sentiment Annotation Tool for Social Media
Tanel Temel
;
Fatih Samet Çetin
;
Meltem Yanık
...
In: http://wing.comp.nus.edu.sg/~antho/W/W13/W13-2316.pdf
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32
arXiv:cmp-lg/9607027v1 26 Jul 1996Learning Translation Rules From A Bilingual Corpus ⋆
Ilyas Cicekli
;
H. Altay Güvenir
In: http://arxiv.org/pdf/cmp-lg/9607027v1.pdf
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