Abstract

A story is defined as "an actor(s) taking action(s) that culminates in a resolution(s)." In this paper, we investigate the utility of standard keyword based features, statistical features based on shallow-parsing (such as density of POS tags and named entities), and a new set of semantic features to develop a story classifier. This classifier is trained to identify a paragraph as a "story," if the paragraph contains mostly story(ies). Training data is a collection of expert-coded story and non-story paragraphs from RSS feeds from a list of extremist web sites. Our proposed semantic features are based on suitable aggregation and generalization of <Subject, Verb, Object> triplets that can be extracted using a parser. Experimental results show that a model of statistical features alongside memory-based semantic linguistic features achieves the best accuracy with a Support Vector Machine (SVM) classifier.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Pages573-580
Number of pages8
DOIs
StatePublished - Dec 1 2012
Event2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 - Istanbul, Turkey
Duration: Aug 26 2012Aug 29 2012

Publication series

NameProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

Other

Other2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
CountryTurkey
CityIstanbul
Period8/26/128/29/12

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Ceran, B., Karad, R., Mandvekar, A., Corman, S., & Davulcu, H. (2012). A semantic triplet based story classifier. In Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 (pp. 573-580). [6425707] (Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012). https://doi.org/10.1109/ASONAM.2012.97