<html><head><style type='text/css'>p { margin: 0; }</style></head><body><div style='font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000'><h1 class="page__title title" id="page-title">Recursive Deep Learning for Modeling Compositional Meaning in Language</h1><p><span class="event-speaker">
<a href="http://www.socher.org/">Richard Socher</a> </span>
<span class="event-speaker-from">
(<a href="http://www.stanford.edu/">Stanford University</a>) <br></span></p><p><span class="event-speaker-from">Tuesday, March 11, 4:30pm<br></span></p><p><span class="event-speaker-from">Computer science 105</span></p><p><span class="event-speaker-from"><br></span></p><p><br></p><p>Great progress has been made in natural language processing thanks
to many different algorithms, each often specific to one
application. Most learning algorithms force language into simplified
representations such as bag-of-words or fixed-sized windows or require
human-designed features. I will introduce three models based on
recursive neural networks that can learn linguistically plausible
representations of language. These methods jointly learn compositional
features and grammatical sentence structure for parsing or phrase level
sentiment predictions. They can also be used to represent the visual
meaning of a sentence which can be used to find images based on query
sentences or to describe images with a more complex description than
single object names.<br>
<br>
Besides the state-of-the-art performance, the models capture interesting
phenomena in language such as compositionality. For instance, people
easily see that the "with" phrase in "eating spaghetti with a spoon"
specifies a way of eating whereas in "eating spaghetti with some pesto"
it specifies the dish. I show that my model solves these prepositional
attachment problems well thanks to its distributed representations. In
sentiment analysis, a new tensor-based recursive model learns
different types of high level negation and how they can change the
meaning of longer phrases with many positive words. They also learn that
when contrastive conjunctions such as "but" are used the sentiment of
the phrases following them usually dominates.</p><p><br></p>
<p>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 deep learning models
that learn useful features, capture compositional 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, a Microsoft Research
PhD Fellowship in 2012 and a 2013 "Magic Grant" from the Brown
Institute for Media Innovation.</p></div></body></html>