<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this paper, a novel neural network is proposed, which can automatically learn and recall contents from texts, and answer questions about the contents in either a large corpus or a short piece of text. The proposed neural network combines parse trees, semantic networks, and inference models. It contains layers corresponding to sentences, clauses, phrases, words and synonym sets. The neurons in the phrase-layer and the word-layer are labeled with their part-of-speeches and their semantic roles. The proposed neural network is automatically organized to represent the contents in a given text. Its carefully designed structure and algorithms make it able to take advantage of the labels and neurons of synonym sets to build the relationship between the sentences about similar things. The experiments show that the proposed neural network with the labels and the synonym sets has the better performance than the others that do not have the labels or the synonym sets while the other parts and the algorithms are the same. The proposed neural network also shows its ability to tolerate noise, to answer factoid questions, and to solve single-choice questions in an exercise book for non-native English learners in the experiments.</p></abstract>

In this paper, a novel neural network is proposed, which can automatically learn and recall contents from texts, and answer questions about the contents in either a large corpus or a short piece of text. The proposed neural network combines parse trees, semantic networks, and inference models. It contains layers corresponding to sentences, clauses, phrases, words and synonym sets. The neurons in the phrase-layer and the word-layer are labeled with their part-of-speeches and their semantic roles. The proposed neural network is automatically organized to represent the contents in a given text. Its carefully designed structure and algorithms make it able to take advantage of the labels and neurons of synonym sets to build the relationship between the sentences about similar things. The experiments show that the proposed neural network with the labels and the synonym sets has the better performance than the others that do not have the labels or the synonym sets while the other parts and the algorithms are the same. The proposed neural network also shows its ability to tolerate noise, to answer factoid questions, and to solve single-choice questions in an exercise book for non-native English learners in the experiments.

An English Neural Network that Learns Texts, Finds Hidden Knowledge, and Answers Questions

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In this paper, a novel neural network is proposed, which can automatically learn and recall contents from texts, and answer questions about the contents in...

An English Neural Network that Learns Texts, Finds Hidden Knowledge, and Answers Questions

Publié en ligne: 03 mai 2017

Pages: 229 - 242

Reçu: 18 mai 2016

Accepté: 14 sept. 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0016

Mots clés
natural language processing, neural network, question answering, natural language understanding

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

An English Neural Network that Learns Texts, Finds Hidden Knowledge, and Answers Questions

Publié en ligne: 03 mai 2017

Pages: 229 - 242

Reçu: 18 mai 2016

Accepté: 14 sept. 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0016

Mots clésnatural language processing, neural network, question answering, natural language understanding

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
natural language processing, neural network, question answering, natural language understanding