Next Article in Journal
Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation
Previous Article in Journal
Some Improvements to a Third Order Variant of Newton’s Method from Simpson’s Rule
Article Menu

Export Article

Open AccessArticle
Algorithms 2015, 8(3), 562-572; doi:10.3390/a8030562

Modeling Documents with Event Model

1
Tsinghua University, Beijing 100000, China
2
School of Mechanical Engineering, Shandong University, Jinan 250061, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jun-Bao Li
Received: 3 May 2015 / Accepted: 23 July 2015 / Published: 4 August 2015
View Full-Text   |   Download PDF [1111 KB, uploaded 4 August 2015]   |  

Abstract

Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks. View Full-Text
Keywords: deep learning; neurolinguistics; topic model; Event Model; document retrieval deep learning; neurolinguistics; topic model; Event Model; document retrieval
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, L.; Zhao, G.; Sun, D. Modeling Documents with Event Model. Algorithms 2015, 8, 562-572.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top