Abstract
With the growing interests in social networking, the interaction of social actors evolved to a source of knowledge in which it become possible to perform context aware reasoning. The information extraction from social networking specially Twitter and Facebook is on of the problem in this area. To extract text from social networking, we need several lexical features and large scale word clustering. We attempt to expand existing tokenizer and to develop our own tagger in order to support the incorrect words currently in existence in Facebook and Twitter. Our goal in this work is to benefit of the lexical features developed for Twitter and online conversational text in previous works, to design and to develop an extraction model for constructing a huge knowledge based on actions.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 161-166 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509043149 |
| DOIs | |
| Publication status | Published - Mar 10 2017 |
| Event | 16th IEEE International Conference on Computer and Information Technology, CIT 2016 - Nadi, Fiji Duration: Dec 7 2016 → Dec 10 2016 |
Other
| Other | 16th IEEE International Conference on Computer and Information Technology, CIT 2016 |
|---|---|
| Country | Fiji |
| City | Nadi |
| Period | 12/7/16 → 12/10/16 |
Keywords
- Information extraction
- Natural language processing
- Part-of-speech tagging
- Social networking
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Information Systems
- Safety, Risk, Reliability and Quality
Cite this
Extracting actions with improved part of speech tagging for social networking texts. / Nouira, Ameni Youssfi; Jamoussi, Yassine; Hajjami, Henda Ben Ghezela.
Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 161-166 7876332.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Extracting actions with improved part of speech tagging for social networking texts
AU - Nouira, Ameni Youssfi
AU - Jamoussi, Yassine
AU - Hajjami, Henda Ben Ghezela
PY - 2017/3/10
Y1 - 2017/3/10
N2 - With the growing interests in social networking, the interaction of social actors evolved to a source of knowledge in which it become possible to perform context aware reasoning. The information extraction from social networking specially Twitter and Facebook is on of the problem in this area. To extract text from social networking, we need several lexical features and large scale word clustering. We attempt to expand existing tokenizer and to develop our own tagger in order to support the incorrect words currently in existence in Facebook and Twitter. Our goal in this work is to benefit of the lexical features developed for Twitter and online conversational text in previous works, to design and to develop an extraction model for constructing a huge knowledge based on actions.
AB - With the growing interests in social networking, the interaction of social actors evolved to a source of knowledge in which it become possible to perform context aware reasoning. The information extraction from social networking specially Twitter and Facebook is on of the problem in this area. To extract text from social networking, we need several lexical features and large scale word clustering. We attempt to expand existing tokenizer and to develop our own tagger in order to support the incorrect words currently in existence in Facebook and Twitter. Our goal in this work is to benefit of the lexical features developed for Twitter and online conversational text in previous works, to design and to develop an extraction model for constructing a huge knowledge based on actions.
KW - Information extraction
KW - Natural language processing
KW - Part-of-speech tagging
KW - Social networking
UR - http://www.scopus.com/inward/record.url?scp=85017341047&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017341047&partnerID=8YFLogxK
U2 - 10.1109/CIT.2016.109
DO - 10.1109/CIT.2016.109
M3 - Conference contribution
AN - SCOPUS:85017341047
SP - 161
EP - 166
BT - Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -