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Recursive Deep Learning In Natural Language Processing And Computer Vision

Abstract:

Hierarchical and recursive structure is commonly found in different modalities, including natural language sentences and scene images. I will introduce several recursive deep learning models that, unlike standard deep learning methods can learn compositional meaning vector representations for phrases or images.

These recursive neural network based models obtain state-of-the-art performance on a variety of syntactic and semantic language tasks such as parsing, sentiment analysis, paraphrase detection and relation classification for extracting knowledge from the web. Because often no language specific assumptions are made the same architectures can be used for visual scene understanding and object classification from 3d images.

Besides the good performance, the models capture interesting phenomena in language such as compositionality. For instance the models learn that “not good” has worse sentiment than “good” or that high level negation can change the meaning of longer phrases with many positive words. Furthermore, unlike most machine learning approaches that rely on human designed feature sets, features are learned as part of the model.

Bio:

Richard Socher is a PhD student at Stanford working with Chris Manning and Andrew Ng. His research interests are machine learning for NLP and vision. He is interested in developing new models that learn useful features, capture compositional and hierarchical structure in multiple modalities and perform well across different tasks. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011 and a Microsoft Research PhD Fellowship in 2012.