An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Offline strategies for online question answering: answering questions before they are asked
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Randomized algorithms and NLP: using locality sensitive hash function for high speed noun clustering
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Search-based structured prediction
Machine Learning
Unsupervised models for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Specialized models and ranking for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Turning lectures into comic books using linguistically salient gestures
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Gesture salience as a hidden variable for coreference resolution and keyframe extraction
Journal of Artificial Intelligence Research
Shallow semantics for coreference resolution
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
State-of-the-art NLP approaches to coreference resolution: theory and practical recipes
ACLTutorials '09 Tutorial Abstracts of ACL-IJCNLP 2009
Automatically generating Wikipedia articles: a structure-aware approach
ACL '09 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: Volume 1 - Volume 1
Accurate semantic class classifier for coreference resolution
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Predicting the semantic compositionality of prefix verbs
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
End-to-end coreference resolution via hypergraph partitioning
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Coreference resolution with world knowledge
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Narrowing the modeling gap: a cluster-ranking approach to coreference resolution
Journal of Artificial Intelligence Research
Journal of Biomedical Informatics
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Stanford's multi-pass sieve coreference resolution system at the CoNLL-2011 shared task
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
An incremental model for coreference resolution with restrictive antecedent accessibility
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Narrative schema as world knowledge for coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Coreference resolution with loose transitivity constraints
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Blanc: Implementing the rand index for coreference evaluation
Natural Language Engineering
Exploration of coreference resolution: the ACE entity detection and recognition task
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Coreference semantics from web features
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Joint learning for coreference resolution with Markov logic
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Simple maximum entropy models for multilingual coreference resolution
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
Coreference resolution: an empirical study based on SemEval-2010 shared Task 1
Language Resources and Evaluation
Deterministic coreference resolution based on entity-centric, precision-ranked rules
Computational Linguistics
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Entity detection and tracking (EDT) is the task of identifying textual mentions of real-world entities in documents, extending the named entity detection and coreference resolution task by considering mentions other than names (pronouns, definite descriptions, etc.). Like NE tagging and coreference resolution, most solutions to the EDT task separate out the mention detection aspect from the coreference aspect. By doing so, these solutions are limited to using only local features for learning. In contrast, by modeling both aspects of the EDT task simultaneously, we are able to learn using highly complex, non-local features. We develop a new joint EDT model and explore the utility of many features, demonstrating their effectiveness on this task.