35 citations · 133 across the 15 of their papers we have counts for
15 papers
Sentence Simplification Using Paraphrase Corpus for Initialization
Kang Liu, Jipeng Qiang
Neural sentence simplification method based on sequence-to-sequence framework has become the mainstream method for sentence simplification (SS) task. Unfortunately, these methods a…
Sentence Simplification via Large Language Models
Yutao Feng, Jipeng Qiang, Yun Li +2
Sentence Simplification aims to rephrase complex sentences into simpler sentences while retaining original meaning. Large Language models (LLMs) have demonstrated the ability to pe…
Chinese Idiom Paraphrasing
Jipeng Qiang, Yang Li, Chaowei Zhang +4
Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters. Due to the properties of non-compositionality and metaphorical meaning, Chi…
Prompt-Learning for Short Text Classification
Yi Zhu, Xinke Zhou, Jipeng Qiang +3
In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks. Recently, as an effective method for tuning Pre-tr…
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages
Xinyu Lu, Jipeng Qiang, Yun Li +2
The availability of parallel sentence simplification (SS) is scarce for neural SS modelings. We propose an unsupervised method to build SS corpora from large-scale bilingual transl…
Chinese Lexical Simplification
Jipeng Qiang, Xinyu Lu, Yun Li +3
Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent m…
LSBert: A Simple Framework for Lexical Simplification
Jipeng Qiang, Yun Li, Yi Zhu +2
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning, to simplify the sentence. Recently unsupervised…
Lexical Simplification with Pretrained Encoders
Jipeng Qiang, Yun Li, Yi Zhu +2
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification ap…
A simple and effective postprocessing method for image classification
Yan Liu, Yun Li, Yunhao Yuan +1
Whether it is computer vision, natural language processing or speech recognition, the essence of these applications is to obtain powerful feature representations that make downstre…
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey
Qiang Jipeng, Qian Zhenyu, Li Yun +2
Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of…
Improving Neural Text Simplification Model with Simplified Corpora
Jipeng Qiang
Text simplification (TS) can be viewed as monolingual translation task, translating between text variations within a single language. Recent neural TS models draw on insights from…
STTM: A Tool for Short Text Topic Modeling
Jipeng Qiang, Yun Li, Yunhao Yuan +2
Along with the emergence and popularity of social communications on the Internet, topic discovery from short texts becomes fundamental to many applications that require semantic un…
Network-based Distance Metric with Application to Discover Disease Subtypes in Cancer
Jipeng Qiang, Wei Ding, John Quackenbush +1
While we once thought of cancer as single monolithic diseases affecting a specific organ site, we now understand that there are many subtypes of cancer defined by unique patterns o…
Topic Modeling over Short Texts by Incorporating Word Embeddings
Jipeng Qiang, Ping Chen, Tong Wang +1
Inferring topics from the overwhelming amount of short texts becomes a critical but challenging task for many content analysis tasks, such as content charactering, user interest pr…
An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
Tong Wang, Ping Chen, Kevin Amaral +1
Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to…