The Role of Context Types and Dimensionality in Learning Word Embeddings

no code implementations • NAACL 2016 • Oren Melamud, David McClosky, Siddharth Patwardhan, Mohit Bansal

We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP tasks.

Learning Word Embeddings

Syntactic Parse Fusion

1 code implementation • EMNLP 2015 • Do Kook Choe, David McClosky, Eugene Charniak

Constituency Parsing

Parsing Paraphrases with Joint Inference

no code implementations • IJCNLP 2015 • Do Kook Choe, David McClosky

The Stanford CoreNLP Natural Language Processing Toolkit

1 code implementation • ACL 2014 • Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, David McClosky

Coreference Resolution Dependency Parsing +5

Learning Constraints for Consistent Timeline Extraction

no code implementations • EMNLP 2012 • David McClosky, Christopher D. Manning

Question Answering Slot Filling

Effective Self-Training for Parsing

1 code implementation • NAACL 2006 • David McClosky, Eugene Charniak, and Mark Johnson

We present a simple, but surprisingly effective, method of self-training a two-phase parser-reranker system using readily available unlabeled data.

Constituency Parsing