@techreport{TD:101366,
	att_abstract={{We propose a multi-step system for the analysis of children stories
that enables several types of analysis. A hybrid approach is adopted, where pattern-based and statistical methods are used along with utilization of external knowledge sources. This system performs the following story analysis tasks: identification of characters in each story; attribution of quotes to specific story characters; identification of character age, gender and other salient personality attributes; and finally, estimation of the emotion expressed in the quoted material. The different types of analyses were evaluated using several datasets. Promising results are obtained for the quote attribution,
as well as for the gender and age estimation.}},
	att_authors={tm330a},
	att_categories={C_IIS.11},
	att_copyright={{ACL}},
	att_copyright_notice={{The definitive version was published in 2014. {{, 2014-04-27}}{{, http://aclweb.org/}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={Natural_Voices},
	att_tags={story analysis,  emotion,  affect,  gender,  age,  anaphora resolution},
	att_techdoc={true},
	att_techdoc_key={TD:101366},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101366_DS1_2014-01-30T16:54:42.464Z.pdf},
	author={Taniya Mishra},
	institution={{Third Workshop on Computational Linguistics for Literature}},
	month={April},
	title={{From Speaker Identification to Affective Analysis:
A Multi-Step System for Analyzing Children Stories}},
	year=2014,
}