Ilyas Cicekli

Also published as: İlyas Çiçekli


Rule Based Morphological Analyzer of Kazakh Language
Gulshat Kessikbayeva | Ilyas Cicekli
Proceedings of the 2014 Joint Meeting of SIGMORPHON and SIGFSM


TURKSENT: A Sentiment Annotation Tool for Social Media
Gülşen Eryiǧit | Fatih Samet Çetin | Meltem Yanık | Tanel Temel | İlyas Çiçekli
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

A Factoid Question Answering System Using Answer Pattern Matching
Nagehan Pala Er | Ilyas Cicekli
Proceedings of the Sixth International Joint Conference on Natural Language Processing

A Hybrid Morphological Disambiguation System for Turkish
Mucahid Kutlu | Ilyas Cicekli
Proceedings of the Sixth International Joint Conference on Natural Language Processing


Text Summarization of Turkish Texts using Latent Semantic Analysis
Makbule Ozsoy | Ilyas Cicekli | Ferda Alpaslan
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)


Learning Translation Templates with Type Constraints
Ilyas Cicekli
Workshop on example-based machine translation

This paper presents a generalization technique that induces translation templates from given translation examples by replacing differing parts in these examples with typed variables. Since the type of each variable is also inferred during the learning process, each induced template is associated with a set of type constraints. The type constraints that are associated with a translation template restrict the usage of that translation template in certain contexts in order to avoid some of wrong translations. The types of variables are induced using the type lattices designed for both source language and target language. The proposed generalization technique has been implemented as a part of an EBMT system.


Ordering translation templates by assigning confidence factors
Zeynep Öz | Ilyas Cicekli
Proceedings of the Third Conference of the Association for Machine Translation in the Americas: Technical Papers

TTL (Translation Template Learner) algorithm learns lexical level correspondences between two translation examples by using analogical reasoning. The sentences used as translation examples have similar and different parts in the source language which must correspond to the similar and different parts in the target language. Therefore these correspondences are learned as translation templates. The learned translation templates are used in the translation of other sentences. However, we need to assign confidence factors to these translation templates to order translation results with respect to previously assigned confidence factors. This paper proposes a method for assigning confidence factors to translation templates learned by the TTL algorithm. Training data is used for collecting statistical information that will be used in confidence factor assignment process. In this process, each template is assigned a confidence factor according to the statistical information obtained from training data. Furthermore, some template combinations are also assigned confidence factors in order to eliminate certain combinations resulting bad translation.

Generatlon of Simple Turkish Sentences with Systemic-Functional Grammar
Ilyas Cicekli | Turgay Korkrmaz
New Methods in Language Processing and Computational Natural Language Learning

A Language-Independent System for Generating Feature Structures from Interlingua Representations
Murat Temizsoy | Ilyas Cicekli
Natural Language Generation


Tactical Generation in a Free Constituent Order Language
Dilek Zeynep Hakkani | Kemal Oflazer | Ilyas Cicekli
Eighth International Natural Language Generation Workshop