Abstract
Recently, dramatic performance improvement in computing has enabled a breakthrough in machine learning technologies. Against this background, generating distributed representation of discrete symbols such as natural languages and images has attracted considerable interest. In the field of natural language processing, word2vec, a method to generate distributed representations of words is well known and its effectiveness well reported. However, an effective method to generate the distributed representation of sentences and documents has not yet been reported.
In this study, we propose a method of generating the distributed representation of sentences by using an autoencoder based on bi-directional long short-term memory (BiLSTM). To obtain the information and findings that necessary to generate effective representations, the computational experiments are carried out.
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Acknowledgements
This work was supported by JSPS KAKENHI Grant, Grant-in-Aid for Scientific Research(C), 26330282.
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Fukuda, K., Mori, N., Matsumoto, K. (2019). A Novel Sentence Vector Generation Method Based on Autoencoder and Bi-directional LSTM. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_16
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DOI: https://doi.org/10.1007/978-3-319-94649-8_16
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