September 28th, 2011 @ 1130am Richard Socher (Stanford)

Recursive Deep Learning in Natural Language Processing and Computer Vision

Richard Socher (Stanford)

Hierarchical and recursive structure is commonly found in different modalities, including natural language sentences and scene images. I will present some of our recent work on three recursive neural network architectures that learn meaning representations for such hierarchical structure. These models obtain state-of-the-art performance on several language and vision tasks.

The meaning of phrases and sentences is determined by the meanings of its words and the rules of compositionality. We introduce a recursive neural network (RNN) for syntactic parsing which can learn vector representations that capture both syntactic and semantic information of phrases and sentences. For instance, the phrases “declined to comment” and “would not disclose” have similar representations. Since our RNN does not depend on specific assumptions for language, it can also be used to find hierarchical structure in complex scene images. This algorithm obtains state-of-the-art performance for semantic scene segmentation on the Stanford Background and the MSRC datasets and outperforms Gist descriptors for scene classification by 4%.

The ability to identify sentiments about personal experiences, products, movies etc. is crucial to understand user generated content in social networks, blogs or product reviews. The second architecture I will talk about is based on semi-supervised recursive autoencoders (RAE). RAEs learn vector representations for phrases sufficiently well as to outperform other traditional supervised sentiment classification methods on several standard datasets. Lastly, I describe an alternative unsupervised RAE model that can learn features which outperform previous approaches for paraphrase detection on the Microsoft Research Paraphrase corpus.

This talk presents joint work with Andrew Ng and Chris Manning.

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