Kuzman Ganchev


2018

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State-of-the-art Chinese Word Segmentation with Bi-LSTMs
Ji Ma | Kuzman Ganchev | David Weiss
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve better accuracy on many of the popular datasets as compared to models based on more complex neuralnetwork architectures. Furthermore, our error analysis shows that out-of-vocabulary words remain challenging for neural-network models, and many of the remaining errors are unlikely to be fixed through architecture changes. Instead, more effort should be made on exploring resources for further improvement.

2016

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Globally Normalized Transition-Based Neural Networks
Daniel Andor | Chris Alberti | David Weiss | Aliaksei Severyn | Alessandro Presta | Kuzman Ganchev | Slav Petrov | Michael Collins
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Semantic Role Labeling with Neural Network Factors
Nicholas FitzGerald | Oscar Täckström | Kuzman Ganchev | Dipanjan Das
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Efficient Inference and Structured Learning for Semantic Role Labeling
Oscar Täckström | Kuzman Ganchev | Dipanjan Das
Transactions of the Association for Computational Linguistics, Volume 3

We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora.

2014

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Semantic Frame Identification with Distributed Word Representations
Karl Moritz Hermann | Dipanjan Das | Jason Weston | Kuzman Ganchev
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Cross-Lingual Discriminative Learning of Sequence Models with Posterior Regularization
Kuzman Ganchev | Dipanjan Das
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Universal Dependency Annotation for Multilingual Parsing
Ryan McDonald | Joakim Nivre | Yvonne Quirmbach-Brundage | Yoav Goldberg | Dipanjan Das | Kuzman Ganchev | Keith Hall | Slav Petrov | Hao Zhang | Oscar Täckström | Claudia Bedini | Núria Bertomeu Castelló | Jungmee Lee
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Using Search-Logs to Improve Query Tagging
Kuzman Ganchev | Keith Hall | Ryan McDonald | Slav Petrov
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Proceedings of the RANLP 2011 Workshop on Information Extraction and Knowledge Acquisition
Preslav Nakov | Zornitsa Kozareva | Kuzman Ganchev | Jerry Hobbs
Proceedings of the RANLP 2011 Workshop on Information Extraction and Knowledge Acquisition

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Rich Prior Knowledge in Learning for Natural Language Processing
Gregory Druck | Kuzman Ganchev | João Graça
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

2010

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Learning Tractable Word Alignment Models with Complex Constraints
João V. Graça | Kuzman Ganchev | Ben Taskar
Computational Linguistics, Volume 36, Issue 3 - September 2010

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Sparsity in Dependency Grammar Induction
Jennifer Gillenwater | Kuzman Ganchev | João Graça | Fernando Pereira | Ben Taskar
Proceedings of the ACL 2010 Conference Short Papers

2009

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Edlin: an Easy to Read Linear Learning Framework
Kuzman Ganchev | Georgi Georgiev
Proceedings of the International Conference RANLP-2009

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Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields
Georgi Georgiev | Preslav Nakov | Kuzman Ganchev | Petya Osenova | Kiril Simov
Proceedings of the International Conference RANLP-2009

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Tunable Domain-Independent Event Extraction in the MIRA Framework
Georgi Georgiev | Kuzman Ganchev | Vassil Momchev | Deyan Peychev | Preslav Nakov | Angus Roberts
Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task

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A Joint Model for Normalizing Gene and Organism Mentions in Text
Georgi Georgiev | Preslav Nakov | Kuzman Ganchev | Deyan Peychev | Vassil Momchev
Proceedings of the Workshop on Biomedical Information Extraction

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Dependency Grammar Induction via Bitext Projection Constraints
Kuzman Ganchev | Jennifer Gillenwater | Ben Taskar
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

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Better Alignments = Better Translations?
Kuzman Ganchev | João V. Graça | Ben Taskar
Proceedings of ACL-08: HLT

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Small Statistical Models by Random Feature Mixing
Kuzman Ganchev | Mark Dredze
Proceedings of the ACL-08: HLT Workshop on Mobile Language Processing

2007

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Transductive Structured Classification through Constrained Min-Cuts
Kuzman Ganchev | Fernando Pereira
Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms for Natural Language Processing

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Automatic Code Assignment to Medical Text
Koby Crammer | Mark Dredze | Kuzman Ganchev | Partha Pratim Talukdar | Steven Carroll
Biological, translational, and clinical language processing

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Semi-Automated Named Entity Annotation
Kuzman Ganchev | Fernando Pereira | Mark Mandel | Steven Carroll | Peter White
Proceedings of the Linguistic Annotation Workshop

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Frustratingly Hard Domain Adaptation for Dependency Parsing
Mark Dredze | John Blitzer | Partha Pratim Talukdar | Kuzman Ganchev | João Graça | Fernando Pereira
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)