Distinguish polarity in bag-of-words visualization

Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, Alok Choudhary

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

Abstract

Neural network-based BOW models reveal that word-embedding vectors encode strong semantic regularities. However, such models are insensitive to word polarity. We show that, coupled with simple information such as word spellings, word-embedding vectors can preserve both semantic regularity and conceptual polarity without supervision. We then describe a nontrivial modification to the t-distributed stochastic neighbor embedding (t-SNE) algorithm that visualizes these semantic- and polarity-preserving vectors in reduced dimensions. On a real Facebook corpus, our experiments show significant improvement in t-SNE visualization as a result of the proposed modification.

Original languageEnglish (US)
Title of host publication31st AAAI Conference on Artificial Intelligence, AAAI 2017
PublisherAAAI press
Pages3344-3350
Number of pages7
StatePublished - Jan 1 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017

Other

Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period2/4/172/10/17

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Distinguish polarity in bag-of-words visualization'. Together they form a unique fingerprint.

Cite this