TY - GEN
T1 - Multi-document Cohesion Network Analysis
T2 - 21st International Conference on Artificial Intelligence in Education, AIED 2020
AU - Dascalu, Maria Dorinela
AU - Ruseti, Stefan
AU - Dascalu, Mihai
AU - McNamara, Danielle S.
AU - Trausan-Matu, Stefan
N1 - Funding Information:
This research was been funded by the “Semantic Media Analytics – SEMANTIC”, subsidiary contract no. 20176/30.10.2019, from the NETIO project ID: P 40 270, MySMIS Code: 105976, the Operational Programme Human Capital of the Ministry of European Funds through the Financial Agreement 51675/09.07.2019, SMIS code 125125, the Institute of Education Sciences (R305A180144, R305A180261, and R305A190063), and the Office of Naval Research (N00014-17-1-2300 and N00014-19-1-2424). The opinions expressed are those of the authors and do not represent views of the IES or ONR.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Reading comprehension requires readers to connect ideas within and across texts to produce a coherent mental representation. One important factor in that complex process regards the cohesion of the document(s). Here, we tackle the challenge of providing researchers and practitioners with a tool to visualize text cohesion both within (intra) and between (inter) texts. This tool, Multi-document Cohesion Network Analysis (MD-CNA), expands the structure of a CNA graph with lexical overlap links of multiple types, together with coreference links to highlight dependencies between text fragments of different granularities. We introduce two visualizations of the CNA graph that support the visual exploration of intratextual and intertextual links. First, a hierarchical view displays a tree-structure of discourse as a visual illustration of CNA links within a document. Second, a grid view available at paragraph or sentence levels displays links both within and between documents, thus ensuring ease of visualization for links spanning across multiple documents. Two use cases are provided to evaluate key functionalities and insights for each type of visualization.
AB - Reading comprehension requires readers to connect ideas within and across texts to produce a coherent mental representation. One important factor in that complex process regards the cohesion of the document(s). Here, we tackle the challenge of providing researchers and practitioners with a tool to visualize text cohesion both within (intra) and between (inter) texts. This tool, Multi-document Cohesion Network Analysis (MD-CNA), expands the structure of a CNA graph with lexical overlap links of multiple types, together with coreference links to highlight dependencies between text fragments of different granularities. We introduce two visualizations of the CNA graph that support the visual exploration of intratextual and intertextual links. First, a hierarchical view displays a tree-structure of discourse as a visual illustration of CNA links within a document. Second, a grid view available at paragraph or sentence levels displays links both within and between documents, thus ensuring ease of visualization for links spanning across multiple documents. Two use cases are provided to evaluate key functionalities and insights for each type of visualization.
KW - Cohesion Network Analysis
KW - Coreference links
KW - Graph visualizations
KW - Lexical overlap links
KW - Semantic links
UR - http://www.scopus.com/inward/record.url?scp=85088553812&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-52240-7_15
DO - 10.1007/978-3-030-52240-7_15
M3 - Conference contribution
AN - SCOPUS:85088553812
SN - 9783030522391
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 80
EP - 85
BT - Artificial Intelligence in Education - 21st International Conference, AIED 2020, Proceedings
A2 - Bittencourt, Ig Ibert
A2 - Cukurova, Mutlu
A2 - Luckin, Rose
A2 - Muldner, Kasia
A2 - Millán, Eva
PB - Springer
Y2 - 6 July 2020 through 10 July 2020
ER -