Author image not provided
 Chenliang Li

Authors:
Add personal information
  Affiliation history
Bibliometrics: publication history
Average citations per article3.33
Citation Count20
Publication count6
Publication years2014-2017
Available for download4
Average downloads per article876.00
Downloads (cumulative)3,504
Downloads (12 Months)1,494
Downloads (6 Weeks)111
SEARCH
ROLE
Arrow RightAuthor only


AUTHOR'S COLLEAGUES
See all colleagues of this author




BOOKMARK & SHARE


6 results found Export Results: bibtexendnoteacmrefcsv

Result 1 – 6 of 6
Sort by:

1 published by ACM
August 2017 ACM Transactions on Information Systems (TOIS): Volume 36 Issue 2, September 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 43,   Downloads (12 Months): 281,   Downloads (Overall): 281

Full text available: PDFPDF
Many applications require semantic understanding of short texts, and inferring discriminative and coherent latent topics is a critical and fundamental task in these applications. Conventional topic models largely rely on word co-occurrences to derive topics from a collection of documents. However, due to the length of each document, short texts ...
Keywords: Topic model, short texts, word embeddings

2
July 2017 Journal of the Association for Information Science and Technology: Volume 68 Issue 7, July 2017
Publisher: John Wiley & Sons, Inc.
Bibliometrics:
Citation Count: 0

Twitter has attracted billions of users for life logging and sharing activities and opinions. In their tweets, users often reveal their location information and short-term visiting histories or plans. Capturing user's short-term activities could benefit many applications for providing the right context at the right time and location. In this ...

3
July 2017 Journal of the Association for Information Science and Technology: Volume 68 Issue 7, July 2017
Publisher: John Wiley & Sons, Inc.
Bibliometrics:
Citation Count: 0

Twitter has attracted billions of users for life logging and sharing activities and opinions. In their tweets, users often reveal their location information and short-term visiting histories or plans. Capturing user's short-term activities could benefit many applications for providing the right context at the right time and location. In this ...

4 published by ACM
October 2016 CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 22,   Downloads (12 Months): 334,   Downloads (Overall): 471

Full text available: PDFPDF
Developing text classifiers often requires a large number of labeled documents as training examples. However, manually labeling documents is costly and time-consuming. Recently, a few methods have been proposed to label documents by using a small set of relevant keywords for each category, known as dataless text classification . In ...
Keywords: dataless text classification, text analysis, topic modeling

5 published by ACM
July 2016 SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 41,   Downloads (12 Months): 741,   Downloads (Overall): 1,493

Full text available: PDFPDF
For many applications that require semantic understanding of short texts, inferring discriminative and coherent latent topics from short texts is a critical and fundamental task. Conventional topic models largely rely on word co-occurrences to derive topics from a collection of documents. However, due to the length of each document, short ...
Keywords: short texts, topic model, word embeddings

6 published by ACM
July 2014 SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 13
Downloads (6 Weeks): 5,   Downloads (12 Months): 138,   Downloads (Overall): 1,259

Full text available: PDFPDF
Twitter is a popular platform for sharing activities, plans, and opinions. Through tweets, users often reveal their location information and short term visiting plans. In this paper, we are interested in extracting fine-grained locations mentioned in tweets with temporal awareness. More specifically, we like to extract each point-of-interest (POI) mention ...
Keywords: temporal awareness, twitter, crf, poi, tweet, location extraction



The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us