Papers by Mika Hasegawa

2 papers
Social Image Tags as a Source of Word Embeddings: A Task-oriented Evaluation (L18-1)

Copied to clipboard

Challenge: Distributional hypothesis-based word representations lack perceptual and empirical knowledge.
Approach: They evaluate the effectiveness of social image tags in generating word embeddings . they find that generated word embeds exhibit somewhat different behaviors from corpus-originated representations - authors .
Outcome: The generated word embeddings exhibit comparable performance with corpus-originated representations.
Word Attribute Prediction Enhanced by Lexical Entailment Tasks (2020.lrec-1)

Copied to clipboard

Challenge: a semantic attribute is associated with a designated dimension in attribute-based vector representations . semantic attributes are created by psychological experimental settings involving human annotators . a conceptual attribute of a concept dictates a specific semantic aspect of the concept .
Approach: They propose a two-stage neural network architecture that fine-tunes attribute representations by employing supervised entailment tasks.
Outcome: The proposed method improves performance of semantic/visual similarity/relatedness evaluation tasks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations