TY - GEN
T1 - Automated Model of Comprehension V2.0
AU - Corlatescu, Dragos Georgian
AU - Dascalu, Mihai
AU - McNamara, Danielle S.
N1 - Funding Information:
Acknowledgments. This research was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number TE 70 PN-III-P1-1.1-TE-2019-2209, ATES – “Automated Text Evaluation and Simplification”, the Institute of Education Sciences (R305A180144 and R305A180261), and the Office of Naval Research (N00014-17-1-2300; N00014-20-1-2623). The opinions expressed are those of the authors and do not represent views of the IES or ONR.
Publisher Copyright:
© Springer Nature Switzerland AG 2021.
PY - 2021
Y1 - 2021
N2 - Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader’s mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of comprehension (AMoC) simulates the construction of readers’ mental representations of text by building syntactic and semantic relations between words, coupled with inferences of related concepts that rely on various automated semantic models. This paper introduces the second version of AMoC that builds upon the initial model with a revised processing pipeline in Python leveraging state-of-the-art NLP models, additional heuristics for improved representations, as well as a new radiant graph visualization of the comprehension model.
AB - Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader’s mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of comprehension (AMoC) simulates the construction of readers’ mental representations of text by building syntactic and semantic relations between words, coupled with inferences of related concepts that rely on various automated semantic models. This paper introduces the second version of AMoC that builds upon the initial model with a revised processing pipeline in Python leveraging state-of-the-art NLP models, additional heuristics for improved representations, as well as a new radiant graph visualization of the comprehension model.
KW - Comprehension model
KW - Lexical dependencies
KW - Natural language processing
KW - Semantic links
UR - http://www.scopus.com/inward/record.url?scp=85126796070&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-78270-2_21
DO - 10.1007/978-3-030-78270-2_21
M3 - Conference contribution
AN - SCOPUS:85126796070
SN - 9783030782696
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 119
EP - 123
BT - Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings
A2 - Roll, Ido
A2 - McNamara, Danielle
A2 - Sosnovsky, Sergey
A2 - Luckin, Rose
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Artificial Intelligence in Education, AIED 2021
Y2 - 14 June 2021 through 18 June 2021
ER -