- Tytuł:
- An English neural network that learns texts, finds hidden knowledge, and answers questions
- Autorzy:
-
Ke, Y.
Hagiwara, M. - Powiązania:
- https://bibliotekanauki.pl/articles/91771.pdf
- Data publikacji:
- 2017
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
natural language processing
neural network
question answering
natural language understanding - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 4; 229-242
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł