23 citations · 96 across the 34 of their papers we have counts for
9 papers · 1 filter
Zipfian Whitening
Sho Yokoi, Han Bao, Hiroto Kurita +1
The word embedding space in neural models is skewed, and correcting this can improve task performance. We point out that most approaches for modeling, correcting, and measuring the…
Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings
Hiroaki Yamagiwa, Yusuke Takase, Hidetoshi Shimodaira
Word embedding is one of the most important components in natural language processing, but interpreting high-dimensional embeddings remains a challenging problem. To address this p…
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
Yihua Zhu, Hidetoshi Shimodaira
The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts. While rotation-based methods…
Discovering Universal Geometry in Embeddings with ICA
Hiroaki Yamagiwa, Momose Oyama, Hidetoshi Shimodaira
This study utilizes Independent Component Analysis (ICA) to unveil a consistent semantic structure within embeddings of words or images. Our approach extracts independent semantic…
3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding
Yihua Zhu, Hidetoshi Shimodaira
The main objective of Knowledge Graph (KG) embeddings is to learn low-dimensional representations of entities and relations, enabling the prediction of missing facts. A significant…
Norm of Word Embedding Encodes Information Gain
Momose Oyama, Sho Yokoi, Hidetoshi Shimodaira
Distributed representations of words encode lexical semantic information, but what type of information is encoded and how? Focusing on the skip-gram with negative-sampling method,…
Improving word mover's distance by leveraging self-attention matrix
Hiroaki Yamagiwa, Sho Yokoi, Hidetoshi Shimodaira
Measuring the semantic similarity between two sentences is still an important task. The word mover's distance (WMD) computes the similarity via the optimal alignment between the se…
Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings
Masahiro Naito, Sho Yokoi, Geewook Kim +1
It is well-known that typical word embedding methods such as Word2Vec and GloVe have the property that the meaning can be composed by adding up the embeddings (additive composition…
Segmentation-free Compositional -gram Embedding
Geewook Kim, Kazuki Fukui, Hidetoshi Shimodaira
We propose a new type of representation learning method that models words, phrases and sentences seamlessly. Our method does not depend on word segmentation and any human-annotated…