Tim Oates


Locality Preserving Loss: Neighbors that Live together, Align together
Ashwinkumar Ganesan | Francis Ferraro | Tim Oates
Proceedings of the Second Workshop on Domain Adaptation for NLP

We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations. Given two pretrained embedding manifolds, LPL optimizes a model to project an embedding and maintain its local neighborhood while aligning one manifold to another. This reduces the overall size of the dataset required to align the two in tasks such as crosslingual word alignment. We show that the LPL-based alignment between input vector spaces acts as a regularizer, leading to better and consistent accuracy than the baseline, especially when the size of the training set is small. We demonstrate the effectiveness of LPL-optimized alignment on semantic text similarity (STS), natural language inference (SNLI), multi-genre language inference (MNLI) and cross-lingual word alignment (CLA) showing consistent improvements, finding up to 16% improvement over our baseline in lower resource settings.

Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment
Ashwinkumar Ganesan | Francis Ferraro | Tim Oates
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification
Hang Gao | Tim Oates
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


KELVIN: a tool for automated knowledge base construction
Paul McNamee | James Mayfield | Tim Finin | Tim Oates | Dawn Lawrie | Tan Xu | Douglas Oard
Proceedings of the 2013 NAACL HLT Demonstration Session


A Context-Aware Approach to Entity Linking
Veselin Stoyanov | James Mayfield | Tan Xu | Douglas Oard | Dawn Lawrie | Tim Oates | Tim Finin
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)


Mining Script-Like Structures from the Web
Niels Kasch | Tim Oates
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading

We’re Not in Kansas Anymore: Detecting Domain Changes in Streams
Mark Dredze | Tim Oates | Christine Piatko
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing


Grounding Word Meanings in Sensor Data: Dealing with Referential Uncertainty
Tim Oates
Proceedings of the HLT-NAACL 2003 Workshop on Learning Word Meaning from Non-Linguistic Data