Login Help

year = 2014 (29 entries)  Select: All None   Action: Show BibTeX

Mark A. Finlayson, Floris Bex, Pablo Gervas and Deniz Yuret, editors. 2014. Literary and Linguistic Computing: Special Issue on Computational Models of Narrative, vol 29, no 4, December. Oxford University Press. [ai.ku] url google scholar
Mehmet Ali Yatbaz, Volkan Cirik, Aylin Küntay and Deniz Yuret. 2014. Paradigmatic representations outperform syntagmatic representations in distributional learning of grammatical categories. In BUCLD, November. [ai.ku] google scholar
Danqi Chen and Christopher Manning. 2014. A Fast and Accurate Dependency Parser using Neural Networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 740--750, Doha, Qatar, October. Association for Computational Linguistics. [dparse] url google scholar
Onur Varol, Deniz Yuret, Burak Erman and Alkan Kabakçıoğlu. 2014. Mode coupling points to functionally important residues in myosin II. Proteins: Structure, Function, and Bioinformatics, vol 82, no 9, pp 1777--1786, September. [ai.ku] url google scholar
Deniz Yuret, Mehmet Ali Yatbaz and Enis Sert. 2014. Unsupervised Instance-Based Part of Speech Induction Using Probable Substitutes. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp 2303--2313, Dublin, Ireland, August. Dublin City University and Association for Computational Linguistics. [ai.ku] url google scholar
Volkan Cirik, Husnu Sensoy and Deniz Yuret. 2014. Parsing Without Words or POS Tags: Dense Word Vectors Capture Sufficient Syntactic Information. In The LTI Student Research Symposium), August. [ai.ku] url url google scholar
Volkan Cirik and Deniz Yuret. 2014. Context-Aware Word Vectors using Substitute Word Distributions. In The LTI Student Research Symposium), August. [ai.ku] url url google scholar
Osman Başkaya. 2014. AI-KU: Using Co-Occurrence Modeling for Semantic Similarity. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp 92--96, Dublin, Ireland, August. Association for Computational Linguistics and Dublin City University. [ai.ku] url google scholar
Wenliang Chen, Yue Zhang and Min Zhang. 2014. Feature Embedding for Dependency Parsing. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp 816--826, Dublin, Ireland, August. Dublin City University and Association for Computational Linguistics. [dparse, dparse.graph] url google scholar
Volkan Cirik. 2014. Analysis of SCODE Word Embeddings based on Substitute Distributions in Supervised Tasks. MS Thesis, August. Koç University. [ai.ku] url google scholar
Volkan Cirik and Deniz Yuret. 2014. Substitute Based SCODE Word Embeddings in Supervised NLP Tasks, Jul. ArXiv e-prints. [ai.ku] url google scholar
Tao Lei, Yu Xin, Yuan Zhang, Regina Barzilay and Tommi Jaakkola. 2014. Low-Rank Tensors for Scoring Dependency Structures. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1381--1391, Baltimore, Maryland, June. Association for Computational Linguistics. [dparse] url google scholar
Oren Melamud, Ido Dagan, Jacob Goldberger, Idan Szpektor and Deniz Yuret. 2014. Probabilistic Modeling of Joint-context in Distributional Similarity. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning, pp 181--190, Ann Arbor, Michigan, June. Association for Computational Linguistics. [ai.ku] url pdf google scholar
Mohit Bansal, Kevin Gimpel and Karen Livescu. 2014. Tailoring Continuous Word Representations for Dependency Parsing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 809--815, Baltimore, Maryland, June. Association for Computational Linguistics. [dparse, dparse.graph] url google scholar
Emre Unal. 2014. A Language Visualization System. MS Thesis, March. Koç University. [ai.ku] pdf pdf google scholar
Stephane Canu. 2014. Understanding SVM, February. Lectures given at the Institute of Mathematics and Statistics, University of Sao Paulo. (slides and code). [perceptron] url google scholar
Mehmet Ali Yatbaz. 2014. Linguistic Category Induction and Tagging Using the Paradigmatic Context Representations with Substitute Words. PhD Thesis, February. Koç University. [ai.ku] pdf pdf url google scholar
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, vol 15, no 1, pp 1929--1958. JMLR. org. [nnets, 5star] google scholar
Mark A. Finlayson, Floris Bex, Pablo Gervás and Deniz Yuret. 2014. Special Issue: Computational Models of Narrative. Literary and Linguistic Computing, vol 29, no 4, pp 465-466. [ai.ku] url url google scholar
Ethem Alpaydın. 2014. Introduction to Machine Learning. The MIT Press. [book.ml] pdf google scholar books
Matthew Honnibal. 2014. Parsing English with 500 lines of Python. (blog post). [dparse] url url pdf google scholar
Art Friedman. 2014. Quantum Mechanics: The Theoretical Minimum. Basic Books. [ebook] epub google scholar books
Max Tegmark. 2014. Our Mathematical Universe: My Quest for the Ultimate Nature of Reality. Knopf Doubleday Publishing Group. [ebook] epub google scholar books
Pablo Gervas. 2014. Composing Narrative Discourse for Stories of Many Characters: a Case Study over a Chess Game . Literary and Linguistic Computing. (submitted). [semparse] pdf google scholar
Daniel Suarez. 2014. Influx. Penguin Group US. [ebook] epub google scholar books
Douglas E. Richards. 2014. Mind's Eye. Paragon Press. [ebook] epub google scholar books
Shai Shalev-Shwartz and S. Ben-David. 2014. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. [book.ml, ebook, perceptron] url pdf google scholar books
Manuel Fernández-Delgado , Eva Cernadas , Senén Barro , Jorge Ribeiro and José Neves . 2014. Direct Kernel Perceptron (DKP): Ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation. Neural Networks, vol 50, no 0, pp 60 - 71. cit 2. [perceptron] url google scholar
Ian J. Goodfellow, Yoshua Bengio and Aaron Courville. 2014. Deep Learning. (online draft). [book.ml, ebook] url pdf google scholar books

x$Id: bibtex.php,v 1.58 2018/05/27 10:47:19 dyuret Exp dyuret $   download