Multi-Document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links
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| Title: | Multi-Document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links |
|---|---|
| Language: | English |
| Authors: | Dascalu, Marina-Dorinela, Ruseti, Stefan, Dascalu, Mihai, McNamara, Danielle, Trausan-Matu, Stefan |
| Source: | Grantee Submission. 2020. |
| Peer Reviewed: | Y |
| Page Count: | 6 |
| Publication Date: | 2020 |
| Sponsoring Agency: | Institute of Education Sciences (ED) Office of Naval Research (ONR) (DOD) |
| Contract Number: | R305A180144 R305A180261 R305A190063 N000141712300 N000141912424 |
| Document Type: | Reports - Research |
| Descriptors: | Network Analysis, Graphs, Connected Discourse, Reading Comprehension, Natural Language Processing, Semantics |
| DOI: | 10.1007/978-3-030-52240-7_15 |
| Abstract: | 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. [This is a chapter in: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (Eds) "Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science," v12164 (p80-85). Cham, Switzerland: Springer Nature Switzerland.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2021 |
| Accession Number: | ED613628 |
| Database: | ERIC |
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