Title | ||
---|---|---|
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations. |
Abstract | ||
---|---|---|
In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings. Attributes can correspond to a wide variety of concepts, such as document indicators (to learn sentence vectors), language indicators (to learn distributed language representations), meta-data and side information (such as the age, gender and industry of a blogger) or representations of authors. We describe a third-order model where word context and attribute vectors interact multiplicatively to predict the next word in a sequence. This leads to the notion of conditional word similarity: how meanings of words change when conditioned on different attributes. We perform several experimental tasks including sentiment classification, cross-lingual document classification, and blog authorship attribution. We also qualitatively evaluate conditional word neighbours and attribute-conditioned text generation. |
Year | Venue | DocType |
---|---|---|
2014 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014) | Journal |
Volume | ISSN | Citations |
27 | 1049-5258 | 27 |
PageRank | References | Authors |
1.68 | 22 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kiros, Ryan | 1 | 2265 | 94.80 |
Richard S. Zemel | 2 | 4958 | 425.68 |
Ruslan Salakhutdinov | 3 | 12190 | 764.15 |
Salakhutdinov, Russ R. | 4 | 27 | 1.68 |