Test:2008 AUnifiedArchitectureforNaturalL

From GM-RKB
Jump to navigation Jump to search

Subject Headings:

Notes

Cited By


Quotes

Abstract

We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in stateof-the-art performance.

BibTeX

@inproceedings{2008_AUnifiedArchitectureforNaturalL,
  author    = {Ronan Collobert and
               Jason Weston},
  editor    = {William W. Cohen and
               Andrew McCallum and
               Sam T. Roweis},
  title     = {A unified architecture for natural language processing: deep neural
               networks with multitask learning},
  booktitle = {Proceedings of the Twenty-Fifth International Conference in Machine Learning
               (ICML 2008)},
  series    = {ACM International Conference Proceeding Series},
  volume    = {307},
  pages     = {160--167},
  publisher = {{ACM}},
  year      = {2008},
  url       = {https://doi.org/10.1145/1390156.1390177},
  doi       = {10.1145/1390156.1390177},
}


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 AUnifiedArchitectureforNaturalLRonan Collobert
Jason Weston
A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning