Paper Info
Title
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network.
Abstract
Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retrieval. We introduce a model that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nuanced semantics. Our model is based on an extended Dynamic Convolution Neural Network, which learns convolution filters at both the sentence and document level, hierarchically learning to capture and compose low level lexical features into high level semantic concepts. We demonstrate the effectiveness of this model on a range of document modelling tasks, achieving strong results with no feature engineering and with a more compact model. Inspired by recent advances in visualising deep convolution networks for computer vision, we present a novel visualisation technique for our document networks which not only provides insight into their learning process, but also can be interpreted to produce a compelling automatic summarisation system for texts.
Year
Venue
Field
2014
CoRR
Visualisation technique,Vector space,Embedding,Computer science,Convolutional neural network,Convolution,Feature engineering,Artificial intelligence,Natural language processing,Sentence,Machine learning,Semantics
DocType
Volume
Citations 
Journal
abs/1406.3830
22
PageRank 
References 
Authors
1.26
22
5
Name
Order
Citations
PageRank
Misha Denil139726.18
Alban Demiraj2301.85
Nal Kalchbrenner33662149.32
Phil Blunsom43130152.18
Nando De Freitas53284273.68