Economic worth-aware word embeddings

Yusan Lin, Peifeng Yin, Wang Chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Knowing the perceived economic value of words is often desirable for applications such as product naming and pricing. However, there is a lack of understanding on the underlying economic worths of words, even though we have seen some breakthrough on learning the semantics of words. In this work, we bridge this gap by proposing a joint-task neural network model, Word Worth Model (WWM), to learn word embedding that captures the underlying economic worths. Through the design of WWM, we incorporate contextual factors, e.g., product's brand name and restaurant's city, that may affect the aggregated monetary value of a textual item. Via a comprehensive evaluation, we show that, compared with other baselines, WWM accurately predicts missing words when given target words. We also show that the learned embeddings of both words and contextual factors reflect well the underlying economic worths through various visualization analyses.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020
EditorsGeoff Webb, Zhongfei Zhang, Vincent S. Tseng, Graham Williams, Michalis Vlachos, Longbing Cao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages344-353
Number of pages10
ISBN (Electronic)9781728182063
DOIs
StatePublished - Oct 2020
Event7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020 - Virtual, Sydney, Australia
Duration: Oct 6 2020Oct 9 2020

Publication series

NameProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020

Conference

Conference7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020
CountryAustralia
CityVirtual, Sydney
Period10/6/2010/9/20

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Decision Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Analysis
  • Discrete Mathematics and Combinatorics

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