activity
20162019
most citedRadical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese

10 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2019

Transfer Learning with Sparse Associative Memories

Quentin Jodelet, Vincent Gripon, Masafumi Hagiwara

In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this lay…

cs.CL2017★ 10 cited

Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese

Yuanzhi Ke, Masafumi Hagiwara

The character vocabulary can be very large in non-alphabetic languages such as Chinese and Japanese, which makes neural network models huge to process such languages. We explored a…

cs.CL2017

Improve Lexicon-based Word Embeddings By Word Sense Disambiguation

Yuanzhi Ke, Masafumi Hagiwara

There have been some works that learn a lexicon together with the corpus to improve the word embeddings. However, they either model the lexicon separately but update the neural net…

cs.CL2016

Alleviating Overfitting for Polysemous Words for Word Representation Estimation Using Lexicons

Yuanzhi Ke, Masafumi Hagiwara

Though there are some works on improving distributed word representations using lexicons, the improper overfitting of the words that have multiple meanings is a remaining issue det…

cs.CL2016

Fuzzy paraphrases in learning word representations with a lexicon

Yuanzhi Ke, Masafumi Hagiwara

A synonym of a polysemous word is usually only the paraphrase of one sense among many. When lexicons are used to improve vector-space word representations, such paraphrases are unr…