Ferhan Türe

Also published as: Ferhan Ture


2018

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SyntaViz: Visualizing Voice Queries through a Syntax-Driven Hierarchical Ontology
Md Iftekhar Tanveer | Ferhan Ture
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

This paper describes SyntaViz, a visualization interface specifically designed for analyzing natural-language queries that were created by users of a voice-enabled product. SyntaViz provides a platform for browsing the ontology of user queries from a syntax-driven perspective, providing quick access to high-impact failure points of the existing intent understanding system and evidence for data-driven decisions in the development cycle. A case study on Xfinity X1 (a voice-enabled entertainment platform from Comcast) reveals that SyntaViz helps developers identify multiple action items in a short amount of time without any special training. SyntaViz has been open-sourced for the benefit of the community.

2017

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No Need to Pay Attention: Simple Recurrent Neural Networks Work!
Ferhan Ture | Oliver Jojic
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65%–76% accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results — even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast’s X1 entertainment platform with millions of users every day.

2016

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Learning to Translate for Multilingual Question Answering
Ferhan Ture | Elizabeth Boschee
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2014

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Learning to Translate: A Query-Specific Combination Approach for Cross-Lingual Information Retrieval
Ferhan Ture | Elizabeth Boschee
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Towards Efficient Large-Scale Feature-Rich Statistical Machine Translation
Vladimir Eidelman | Ke Wu | Ferhan Ture | Philip Resnik | Jimmy Lin
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Mr. MIRA: Open-Source Large-Margin Structured Learning on MapReduce
Vladimir Eidelman | Ke Wu | Ferhan Ture | Philip Resnik | Jimmy Lin
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2012

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Combining Statistical Translation Techniques for Cross-Language Information Retrieval
Ferhan Ture | Jimmy Lin | Douglas Oard
Proceedings of COLING 2012

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Encouraging Consistent Translation Choices
Ferhan Ture | Douglas W. Oard | Philip Resnik
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Why Not Grab a Free Lunch? Mining Large Corpora for Parallel Sentences to Improve Translation Modeling
Ferhan Ture | Jimmy Lin
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2010

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cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models
Chris Dyer | Adam Lopez | Juri Ganitkevitch | Jonathan Weese | Ferhan Ture | Phil Blunsom | Hendra Setiawan | Vladimir Eidelman | Philip Resnik
Proceedings of the ACL 2010 System Demonstrations

2006

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Learning Morphological Disambiguation Rules for Turkish
Deniz Yuret | Ferhan Türe
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference