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What's Yours and What's Mine: Determining Intellectual Attribution in Scientific Text

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TLDR
The algorithm and a systematic evaluation of a system which can recognize the most salient textual properties that contribute to the global argumentative structure of a text are presented.
Abstract
We believe that identifying the structure of scientific argumentation in articles can help in tasks such as automatic summarization or the automated construction of citation indexes. One particularly important aspect of this structure is the question of who a given scientific statement is attributed to: other researchers, the field in general, or the authors themselves.We present the algorithm and a systematic evaluation of a system which can recognize the most salient textual properties that contribute to the global argumentative structure of a text. In this paper we concentrate on two particular features, namely the occurrences of prototypical agents and their actions in scientific text.

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Journal ArticleDOI

Learning Subjective Language

TL;DR: This article shows that the density of subjectivity clues in the surrounding context strongly affects how likely it is that a word is subjective, and it provides the results of an annotation study assessing the subjectivity of sentences with high-density features.
Journal Article

Summarizing scientific articles: Experiments with relevance and rhetorical status : Summarization

TL;DR: This article provides a gold standard for summaries of this kind consisting of a substantial corpus of conference articles in computational linguistics annotated with human judgments of the rhetorical status and relevance of each sentence in the articles.
Journal ArticleDOI

Summarizing scientific articles: experiments with relevance and rhetorical status

TL;DR: The authors proposed a strategy for the summarization of scientific articles that concentrates on the rhetorical status of statements in an article: material for summaries is selected in such a way that summaries can highlight the new contribution of the source article and situate it with respect to earlier work.
Proceedings ArticleDOI

Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates

TL;DR: This paper designs computational models for examining how automatically computed stance information can be profitably exploited for reason classification, and demonstrates that sophisticated models of stances and reasons can indeed yield more accurate reason and stance classification results than their simpler counterparts.
Proceedings Article

Identifying Collocations for Recognizing Opinions

Janyce Wiebe
TL;DR: Promising results are shown for a straightforward method of identifying collocational clues of subjectivity, as well as evidence of the usefulness of these clues for recognizing opinionated documents.
References
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Journal ArticleDOI

The measurement of observer agreement for categorical data

TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
Book

Nonparametric statistics for the behavioral sciences

Sidney Siegel
TL;DR: This is the revision of the classic text in the field, adding two new chapters and thoroughly updating all others as discussed by the authors, and the original structure is retained, and the book continues to serve as a combined text/reference.
Book

Content analysis: an introduction to its methodology

TL;DR: History Conceptual Foundations Uses and Kinds of Inference The Logic of Content Analysis Designs Unitizing Sampling Recording Data Languages Constructs for Inference Analytical Techniques The Use of Computers Reliability Validity A Practical Guide
Journal ArticleDOI

Non-Parametric Statistics for the Behavioral Sciences.

Alan Stuart, +1 more
- 01 May 1957 - 
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