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Yi Yang: Efficiently Learning and Applying Dense Feature Representations for NLP

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Published on May 17, 2016

Yi Yang: Efficiently Learning and Applying Dense Feature Representations for Natural Language Processing

Abstract:
With the resurgence of neural networks, low-dimensional dense features have been used in a wide range of natural language processing problems. Specifically, tasks like part-of-speech tagging, dependency parsing and entity linking have been shown to benefit from dense feature representations from both efficiency and effectiveness aspects. In this talk, I will present algorithms for unsupervised domain adaptation, where we train low-dimensional feature embeddings with instances from both source and target domains. I will also talk about how to extend the approach to unsupervised multi-domain adaptation by leveraging metadata domain attributes. I will then introduce a tree-based structured learning model for entity linking, where the model employs a few statistical dense features to jointly detect mentions and disambiguate entities. Finally, I will discuss some promising directions for future research.

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