Di Marco C, Kroon FW, Mercer RE "Using hedges to classify citations in scientific articles " COMPUTING ATTITUDE AND AFFECT IN TEXT: THEORY AND APPLICATIONS 20: 247-263, 2006

[Eugene Garfield]

C. DiMarco : cdimarco at uwaterloo.ca

R.E. Mercer : mercer at csd.uwo.ca



Title: Using hedges to classify citations in scientific articles 

Author(s): Di Marco C, Kroon FW, Mercer RE 

Source: COMPUTING ATTITUDE AND AFFECT IN TEXT: THEORY AND APPLICATIONS 20: 
247-263, 2006 
Book Series: INFORMATION RETRIEVAL SERIES 

Editor(s): Shanahan JG, Qu Y, Wiebe J 
Document Type: Article 
Language: English 
Cited References: 36        
Conference Information: Symposium on Computing Attitude and Affect in Text
Stanford Univ, Stanford, CA, MAR, 2004
AAAI 

Abstract: 
Citations in scientific writing fulfil an important role in creating 
relationships among mutually relevant articles within a research field. 
These inter-article relationships reinforce the argumentation structure 
intrinsic to all scientific writing. Therefore, determining the nature of 
the exact relationship between a citing and cited paper requires an 
understanding of the rhetorical relations within the argumentative context 
in which a citation is placed. To determine these relations automatically, 
we have suggested that various stylistic and rhetorical cues will be 
significant. One such cue that we are studying is the use of hedging to 
modify the affect of a scientific claim. We provide evidence that hedging 
occurs more frequently in citation contexts than in the text as a whole. 
With this information we conjecture that hedging is a significant aspect of 
the rhetorical structure of citation contexts and that the pragmatics of 
hedges may help in determining the rhetorical purpose of citations. A 
citation indexing tool for biomedical literature analysis is introduced. 

1. Scientific Writing, the Need for Affect, and Its Role in Citation 
Analysis. 
2. Hedging in Scientific Writing. 
3. Classifying Citations in Scientific Writing. 
4. Determining the Importance of Hedges in Citation Contexts. 
5. A Citation Indexing Tool for Biomedical Literature Analysis. 
6. Conclusions and Future Work.-


Author Keywords: automatic citation analysis; hedges; rhetoric of science; 
science writing 
KeyWords Plus: CLASSIFICATION 

Addresses: Di Marco C (reprint author), Univ Waterloo, Dept Comp Sci, 
Waterloo, ON N2L 3G1 Canada
Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1 Canada 

Publisher: SPRINGER, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS 
IDS Number: BDW67 

ISBN: 1-4020-4026-1

ANDRADE MA
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Title: The frequency of hedging cues in citation contexts in scientific 
writing 

Author(s): Mercer RE, Di Marco C, Kroon FW 

Source: ADVANCES IN ARTIFICIAL INTELLIGENCE LECTURE NOTES IN ARTIFICIAL 
INTELLIGENCE 3060: 75-88 2004 

Document Type: Article 

Language: English 

Cited References: 14      Times Cited: 0        

Abstract: Citations in scientific writing fulfill an important role in 
creating relationships among mutually relevant articles within a research 
field. These inter-article relationships reinforce the argumentation 
structure that is intrinsic to all scientific writing. Therefore, 
determining the nature of the exact relationship between a citing and cited 
paper requires an understanding of the rhetorical relations within the 
argumentative context in which a citation is placed. To determine these 
relations automatically in scientific writing, we have suggested that 
stylistic and rhetorical cues will be significant. One type of cue that we 
have studied is the discourse cue, which provides cohesion among textual 
components. Another form of rhetorical cue involves hedging to modify the 
affect of a scientific claim. Hedging in scientific writing has been 
extensively studied by Hyland, including cataloging the pragmatic functions 
of the various types of cues. In this paper we show that the hedging cues 
proposed by Hyland occur more frequently in citation contexts than in the 
text as a whole. With this information we conjecture that hedging cues are 
an important aspect of the rhetorical relations found in citation contexts 
and that the pragmatics of hedges may help in determining the purpose of 
citations. 

Addresses: Mercer RE (reprint author), Univ Western Ontario, London, ON N6A 
5B7 Canada
Univ Western Ontario, London, ON N6A 5B7 Canada
Univ Waterloo, Waterloo, ON N2L 3G1 Canada 

E-mail Addresses: mercer at csd.uwo.ca, cdimarco at uwaterloo.ca, 
fwkroon at uwaterloo.ca 

Publisher: SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, 
GERMANY 
 

  
   

 Title: The importance of fine-grained cue phrases in scientific citations 

Author(s): Mercer RE, Di Marco C 

Source: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS LECTURE NOTES IN 
ARTIFICIAL INTELLIGENCE 2671: 550-556 2003 

Document Type: Article 

Language: English 

Cited References: 9      Times Cited: 0        

Abstract: Scientific citations play a crucial role in maintaining the 
network of relationships among mutually relevant articles within a research 
field. Customarily, authors include citations in their papers to indicate 
works that axe foundational in their field, background for their own work, 
or representative of complementary or contradictory research. But, 
determining the nature of the exact relationship between a citing and cited 
paper is often difficult to ascertain. To address this problem, the aim of 
formal citation analysis has been to categorize and, ultimately, 
automatically classify scientific citations. In previous work, Garzone and 
Mercer (2000) presented a system for citation classification that relied on 
characteristic syntactic structure to determine citation category. In this 
present work, we extend this idea to propose that fine-grained cue phrases 
within citation sentences may provide a stylistic basis for just such a 
categorization. 

Addresses: Mercer RE (reprint author), Univ Western Ontario, London, ON N6A 
5B7 Canada
Univ Western Ontario, London, ON N6A 5B7 Canada
Univ Waterloo, Waterloo, ON N2L 3G1 Canada 

Publisher: SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, 
GERMANY 

IDS Number: BX29V 

ISSN: 0302-9743 

 
 

Title: Towards an automated citation classifier 

Author(s): Garzone M, Mercer RE 

Source: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS LECTURE NOTES IN 
ARTIFICIAL INTELLIGENCE 1822: 337-346 2000 

Document Type: Article 

Language: English 

Cited References: 14      Times Cited: 2        

Abstract: Described here is a first attempt to classify citations according 
to function in a fully automatic manner, that is, complete journal articles 
in electronic form are input to the citation classifier and a set of 
citations with their suggested function (chosen from a previously proposed 
scheme of functions) is output. The description consists of a brief 
introduction to the classification scheme, a description of the classifier, 
and a summary of the results of a test of the classifier on real data.  

Addresses: Garzone M (reprint author), Univ Western Ontario, Dept Comp Sci, 
Cognit Engn Lab, London, ON N5Y 4B6 Canada
Univ Western Ontario, Dept Comp Sci, Cognit Engn Lab, London, ON N5Y 4B6 
Canada 

Publisher: SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, 
GERMANY 

IDS Number: BR57V