activity
20182022
most citedEvaluating Word Embedding Models: Methods and Experimental Results

331 citations · 331 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL2020

Efficient Sentence Embedding via Semantic Subspace Analysis

Bin Wang, Fenxiao Chen, Yuncheng Wang +1

A novel sentence embedding method built upon semantic subspace analysis, called semantic subspace sentence embedding (S3E), is proposed in this work. Given the fact that word embed…

cs.CL2020

SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models

Bin Wang, C. -C. Jay Kuo

Sentence embedding is an important research topic in natural language processing (NLP) since it can transfer knowledge to downstream tasks. Meanwhile, a contextualized word represe…

cs.LG2019

Graph Representation Learning: A Survey

Fenxiao Chen, Yuncheng Wang, Bin Wang +1

Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph…

cs.CL2019331 cited

Evaluating Word Embedding Models: Methods and Experimental Results

Bin Wang, Angela Wang, Fenxiao Chen +2

Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work. First, we introduce popular word embedding models an…

cs.CL2018

Graph-based Deep-Tree Recursive Neural Network (DTRNN) for Text Classification

Fenxiao Chen, Bin Wang, C. -C. Jay Kuo

A novel graph-to-tree conversion mechanism called the deep-tree generation (DTG) algorithm is first proposed to predict text data represented by graphs. The DTG method can generate…

cs.CL2018

Post-Processing of Word Representations via Variance Normalization and Dynamic Embedding

Bin Wang, Fenxiao Chen, Angela Wang +1

Although embedded vector representations of words offer impressive performance on many natural language processing (NLP) applications, the information of ordered input sequences is…