J
Jianshu Weng
Researcher at Nanyang Technological University
Publications - 23
Citations - 4694
Jianshu Weng is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Microblogging & Named-entity recognition. The author has an hindex of 14, co-authored 22 publications receiving 4446 citations. Previous affiliations of Jianshu Weng include Singapore Management University & Hewlett-Packard.
Papers
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Proceedings ArticleDOI
TwitterRank: finding topic-sensitive influential twitterers
TL;DR: Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank, which is proposed to measure the influence of users in Twitter.
Book ChapterDOI
Comparing twitter and traditional media using topic models
TL;DR: This paper empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling, and finds interesting and useful findings for downstream IR or DM applications.
Proceedings Article
Event Detection in Twitter
Jianshu Weng,Bu-Sung Lee +1 more
TL;DR: This paper attempts to tackle the challenges of event detection in Twitter with EDCoW (Event Detection with Clustering of Wavelet-based Signals), which builds signals for individual words by applying wavelet analysis on the frequencybased raw signals of the words.
Proceedings ArticleDOI
TwiNER: named entity recognition in targeted twitter stream
TL;DR: A novel 2-step unsupervised NER system for targeted Twitter stream, called TwiNER, which leverages on the global context obtained from Wikipedia and Web N-Gram corpus to partition tweets into valid segments (phrases) using a dynamic programming algorithm.
Journal ArticleDOI
Tweet Segmentation and Its Application to Named Entity Recognition
TL;DR: Experiments show that tweet segmentation quality is significantly improved by learning both global and local contexts compared with using global context alone, and that high accuracy is achieved in named entity recognition by applying segment-based part-of-speech (POS) tagging.