IEICE TRANSACTIONS on Information
Example Phrase Adaptation Method for Customized, Example-Based Dialog System Using User Data and Distributed Word Representations
Summary :
We have developed an adaptation method which allows the customization of example-based dialog systems for individual users by applying “plus” and “minus” operations to the distributed representations obtained using the word2vec method. After retrieving user-related profile information from the Web, named entity extraction is applied to the retrieval results. Words with a high term frequency-inverse document frequency (TF-IDF) score are then adopted as user related words. Next, we calculate the similarity between the distrubuted representations of selected user-related words and nouns in the existing example phrases, using word2vec embedding. We then generate phrases adapted to the user by substituting user-related words for highly similar words in the original example phrases. Word2vec also has a special property which allows the arithmetic operations “plus” and “minus” to be applied to distributed word representations. By applying these operations to words used in the original phrases, we are able to determine which user-related words can be used to replace the original words. The user-related words are then substituted to create customized example phrases. We evaluated the naturalness of the generated phrases and found that the system could generate natural phrases.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.11 pp.2332-2339
- Publication Date
- 2020/11/01
- Publicized
- 2020/07/30
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDP7066
- Type of Manuscript
- PAPER
- Category
- Speech and Hearing
Authors
Norihide KITAOKA
Toyohashi University of Technology
Eichi SETO
Tokushima University
Ryota NISHIMURA
Tokushima University
Keyword
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Norihide KITAOKA, Eichi SETO, Ryota NISHIMURA, "Example Phrase Adaptation Method for Customized, Example-Based Dialog System Using User Data and Distributed Word Representations" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 11, pp. 2332-2339, November 2020, doi: 10.1587/transinf.2020EDP7066.
Abstract: We have developed an adaptation method which allows the customization of example-based dialog systems for individual users by applying “plus” and “minus” operations to the distributed representations obtained using the word2vec method. After retrieving user-related profile information from the Web, named entity extraction is applied to the retrieval results. Words with a high term frequency-inverse document frequency (TF-IDF) score are then adopted as user related words. Next, we calculate the similarity between the distrubuted representations of selected user-related words and nouns in the existing example phrases, using word2vec embedding. We then generate phrases adapted to the user by substituting user-related words for highly similar words in the original example phrases. Word2vec also has a special property which allows the arithmetic operations “plus” and “minus” to be applied to distributed word representations. By applying these operations to words used in the original phrases, we are able to determine which user-related words can be used to replace the original words. The user-related words are then substituted to create customized example phrases. We evaluated the naturalness of the generated phrases and found that the system could generate natural phrases.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7066/_p
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@ARTICLE{e103-d_11_2332,
author={Norihide KITAOKA, Eichi SETO, Ryota NISHIMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Example Phrase Adaptation Method for Customized, Example-Based Dialog System Using User Data and Distributed Word Representations},
year={2020},
volume={E103-D},
number={11},
pages={2332-2339},
abstract={We have developed an adaptation method which allows the customization of example-based dialog systems for individual users by applying “plus” and “minus” operations to the distributed representations obtained using the word2vec method. After retrieving user-related profile information from the Web, named entity extraction is applied to the retrieval results. Words with a high term frequency-inverse document frequency (TF-IDF) score are then adopted as user related words. Next, we calculate the similarity between the distrubuted representations of selected user-related words and nouns in the existing example phrases, using word2vec embedding. We then generate phrases adapted to the user by substituting user-related words for highly similar words in the original example phrases. Word2vec also has a special property which allows the arithmetic operations “plus” and “minus” to be applied to distributed word representations. By applying these operations to words used in the original phrases, we are able to determine which user-related words can be used to replace the original words. The user-related words are then substituted to create customized example phrases. We evaluated the naturalness of the generated phrases and found that the system could generate natural phrases.},
keywords={},
doi={10.1587/transinf.2020EDP7066},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Example Phrase Adaptation Method for Customized, Example-Based Dialog System Using User Data and Distributed Word Representations
T2 - IEICE TRANSACTIONS on Information
SP - 2332
EP - 2339
AU - Norihide KITAOKA
AU - Eichi SETO
AU - Ryota NISHIMURA
PY - 2020
DO - 10.1587/transinf.2020EDP7066
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2020
AB - We have developed an adaptation method which allows the customization of example-based dialog systems for individual users by applying “plus” and “minus” operations to the distributed representations obtained using the word2vec method. After retrieving user-related profile information from the Web, named entity extraction is applied to the retrieval results. Words with a high term frequency-inverse document frequency (TF-IDF) score are then adopted as user related words. Next, we calculate the similarity between the distrubuted representations of selected user-related words and nouns in the existing example phrases, using word2vec embedding. We then generate phrases adapted to the user by substituting user-related words for highly similar words in the original example phrases. Word2vec also has a special property which allows the arithmetic operations “plus” and “minus” to be applied to distributed word representations. By applying these operations to words used in the original phrases, we are able to determine which user-related words can be used to replace the original words. The user-related words are then substituted to create customized example phrases. We evaluated the naturalness of the generated phrases and found that the system could generate natural phrases.
ER -
English
