Moving from news to social media: unsupervised knowledge enrichment for event extraction

Authors
Li, Hao
ORCID
Loading...
Thumbnail Image
Other Contributors
Ji, Heng
Wallace, William A., 1935-
McGuinness, Deborah L.
Adali, Sibel
Liu, Li (Emily)
Issue Date
2015-12
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
The second challenge comes from informal genres such as social media. The context of a social media message is usually short and incomplete (e.g., each tweet has a length limitation of 140 characters). Lacking of context, a single tweet itself usually cannot provide a complete picture of the corresponding events. The third challenge is the informal nature of social media. Social media messages are written in an informal style, which causes the poor performance of NLP tools designed for more formal genres.
Description
December 2015
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.