405 citations · 2.3k across the 116 of their papers we have counts for
13 papers · 1 filter
Bias and Fairness in Chatbots: An Overview
Jintang Xue, Yun-Cheng Wang, Chengwei Wei +3
Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language…
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations
Yun-Cheng Wang, Xiou Ge, Bin Wang +1
Knowledge graph entity typing (KGET) is a task to predict the missing entity types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to solve the KGET task by…
An Overview on Language Models: Recent Developments and Outlook
Chengwei Wei, Yun-Cheng Wang, Bin Wang +1
Language modeling studies the probability distributions over strings of texts. It is one of the most fundamental tasks in natural language processing (NLP). It has been widely used…
SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation
Chengwei Wei, Bin Wang, C. -C. Jay Kuo
Word Mover's Distance (WMD) computes the distance between words and models text similarity with the moving cost between words in two text sequences. Yet, it does not offer good per…
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…
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…
Tree-structured multi-stage principal component analysis (TMPCA): theory and applications
Yuanhang Su, Ruiyuan Lin, C. -C. Jay Kuo
A PCA based sequence-to-vector (seq2vec) dimension reduction method for the text classification problem, called the tree-structured multi-stage principal component analysis (TMPCA)…
Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis
Yuanhang Su, Yuzhong Huang, C. -C. Jay Kuo
A novel text data dimension reduction technique, called the tree-structured multi-linear principal component anal- ysis (TMPCA), is proposed in this work. Being different from trad…