TY - GEN
T1 - Age of exposure
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
AU - Dascalu, Mihai
AU - McNamara, Danielle
AU - Crossley, Scott
AU - Trausan-Matu, Stefan
N1 - Funding Information:
This research was partially supported by the 2008-212578 LTfLL FP7 and POSDRU/159/1.5/S/134398 projects, as well as by the NSF grants 1417997 and 1418378 to Arizona State University. We would also like to thank Lucia Larise Stavarache, Philippe Dessus, Teodor Rosu and Laura Allen for their involvement in the design of AoE
Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - Textual complexity is widely used to assess the difficulty of reading materials and writing quality in student essays. At a lexical level, word complexity can represent a building block for creating a comprehensive model of lexical networks that adequately estimates learners' understanding. In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). AOE indices computationally model the lexical learning process as a function of a learner's experience with language. This study describes a proof of concept based on the on a largescale learning corpus (i.e., TASA). The results indicate that AoE indices yield strong associations with human ratings of age of acquisition, word frequency, entropy, and human lexical response latencies providing evidence of convergent validity.
AB - Textual complexity is widely used to assess the difficulty of reading materials and writing quality in student essays. At a lexical level, word complexity can represent a building block for creating a comprehensive model of lexical networks that adequately estimates learners' understanding. In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). AOE indices computationally model the lexical learning process as a function of a learner's experience with language. This study describes a proof of concept based on the on a largescale learning corpus (i.e., TASA). The results indicate that AoE indices yield strong associations with human ratings of age of acquisition, word frequency, entropy, and human lexical response latencies providing evidence of convergent validity.
UR - http://www.scopus.com/inward/record.url?scp=84996768944&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996768944&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84996768944
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 2928
EP - 2934
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
PB - AAAI press
Y2 - 12 February 2016 through 17 February 2016
ER -