331 citations · 569 across the 47 of their papers we have counts for
9 papers · 1 filter
Just Rank: Rethinking Evaluation with Word and Sentence Similarities
Bin Wang, C. -C. Jay Kuo, Haizhou Li
Word and sentence embeddings are useful feature representations in natural language processing. However, intrinsic evaluation for embeddings lags far behind, and there has been no…
Task-Specific Dependency-based Word Embedding Methods
Chengwei Wei, Bin Wang, C. -C. Jay Kuo
Two task-specific dependency-based word embedding methods are proposed for text classification in this work. In contrast with universal word embedding methods that work for generic…
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…
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…
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…
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…