Using Recurrent Neural Network for Learning Expressive Ontologies

Recently, Neural Networks have been proven extremely effective in many natural language processing tasks such as sentiment analysis, question answering, or machine translation. Aiming to exploit such advantages in the Ontology Learning process, in this technical report we present a detailed description of a Recurrent Neural Network based system to be used to pursue such goal.

[1]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[2]  Yoshua Bengio,et al.  Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding , 2013, INTERSPEECH.

[3]  Phil Blunsom,et al.  Multilingual Distributed Representations without Word Alignment , 2013, ICLR 2014.

[4]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[5]  Christopher Potts,et al.  Recursive Neural Networks Can Learn Logical Semantics , 2014, CVSC.

[6]  Geoffrey Zweig,et al.  Linguistic Regularities in Continuous Space Word Representations , 2013, NAACL.

[7]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[8]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[9]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[10]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[11]  Kam-Fai Wong,et al.  Towards Neural Network-based Reasoning , 2015, ArXiv.

[12]  Peter Haase,et al.  Learning Expressive Ontologies , 2008, Ontology Learning and Population.

[13]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[14]  Wojciech Zaremba,et al.  Learning to Execute , 2014, ArXiv.

[15]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.