An extracting model for constructing actions with improved part-of-speech tagging from social networking texts

Yassine Jamoussi, Ameni Youssfi Nouira

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The recent viral growth of social network systems such as Twitter, Facebook and MySpace have created many interesting and challenging problems to the research community, which enable to perform context aware-reasoning. Social networking is a set of social actors (individuals or organizations) that are connected to provide a set of interaction. We consider, in this paper, the problem of information extraction from social networking specially Twitter and Facebook. 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, and to develop an extraction model for constructing a huge knowledge based on actions.

Original languageEnglish
Title of host publicationProceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages77-81
Number of pages5
ISBN (Electronic)9781509027170
DOIs
Publication statusPublished - Feb 14 2017
Event2017 11th International Conference on Intelligent Systems and Control, ISCO 2017 - Coimbatore, India
Duration: Jan 5 2017Jan 6 2017

Other

Other2017 11th International Conference on Intelligent Systems and Control, ISCO 2017
CountryIndia
CityCoimbatore
Period1/5/171/6/17

Fingerprint

Social Networking
Tagging
Information Extraction
Context-aware
Knowledge-based
Social Networks
Expand
Reasoning
Clustering
Model
Interaction
Text
Speech

Keywords

  • information extraction
  • natural language processing
  • part-of-speech tagging
  • social networking

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Artificial Intelligence
  • Control and Optimization

Cite this

Jamoussi, Y., & Nouira, A. Y. (2017). An extracting model for constructing actions with improved part-of-speech tagging from social networking texts. In Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017 (pp. 77-81). [7855957] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCO.2017.7855957

An extracting model for constructing actions with improved part-of-speech tagging from social networking texts. / Jamoussi, Yassine; Nouira, Ameni Youssfi.

Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 77-81 7855957.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jamoussi, Y & Nouira, AY 2017, An extracting model for constructing actions with improved part-of-speech tagging from social networking texts. in Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017., 7855957, Institute of Electrical and Electronics Engineers Inc., pp. 77-81, 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017, Coimbatore, India, 1/5/17. https://doi.org/10.1109/ISCO.2017.7855957
Jamoussi Y, Nouira AY. An extracting model for constructing actions with improved part-of-speech tagging from social networking texts. In Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 77-81. 7855957 https://doi.org/10.1109/ISCO.2017.7855957
Jamoussi, Yassine ; Nouira, Ameni Youssfi. / An extracting model for constructing actions with improved part-of-speech tagging from social networking texts. Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 77-81
@inproceedings{9c5f6a7a43824aea8c6dc1d69d11ddcb,
title = "An extracting model for constructing actions with improved part-of-speech tagging from social networking texts",
abstract = "The recent viral growth of social network systems such as Twitter, Facebook and MySpace have created many interesting and challenging problems to the research community, which enable to perform context aware-reasoning. Social networking is a set of social actors (individuals or organizations) that are connected to provide a set of interaction. We consider, in this paper, the problem of information extraction from social networking specially Twitter and Facebook. 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, and to develop an extraction model for constructing a huge knowledge based on actions.",
keywords = "information extraction, natural language processing, part-of-speech tagging, social networking",
author = "Yassine Jamoussi and Nouira, {Ameni Youssfi}",
year = "2017",
month = "2",
day = "14",
doi = "10.1109/ISCO.2017.7855957",
language = "English",
pages = "77--81",
booktitle = "Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - An extracting model for constructing actions with improved part-of-speech tagging from social networking texts

AU - Jamoussi, Yassine

AU - Nouira, Ameni Youssfi

PY - 2017/2/14

Y1 - 2017/2/14

N2 - The recent viral growth of social network systems such as Twitter, Facebook and MySpace have created many interesting and challenging problems to the research community, which enable to perform context aware-reasoning. Social networking is a set of social actors (individuals or organizations) that are connected to provide a set of interaction. We consider, in this paper, the problem of information extraction from social networking specially Twitter and Facebook. 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, and to develop an extraction model for constructing a huge knowledge based on actions.

AB - The recent viral growth of social network systems such as Twitter, Facebook and MySpace have created many interesting and challenging problems to the research community, which enable to perform context aware-reasoning. Social networking is a set of social actors (individuals or organizations) that are connected to provide a set of interaction. We consider, in this paper, the problem of information extraction from social networking specially Twitter and Facebook. 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, 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=85015030657&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015030657&partnerID=8YFLogxK

U2 - 10.1109/ISCO.2017.7855957

DO - 10.1109/ISCO.2017.7855957

M3 - Conference contribution

SP - 77

EP - 81

BT - Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -