A Spreading Activation Framework for Tracking Conceptual Complexity of Texts

Ioana Hulpuș, Sanja Štajner, Heiner Stuckenschmidt


Abstract
We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation. Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further activation as the text is sequentially traversed. Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting. We show that our models capture various aspects of conceptual text complexity and significantly outperform current state of the art.
Anthology ID:
P19-1377
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3878–3887
Language:
URL:
https://www.aclweb.org/anthology/P19-1377
DOI:
10.18653/v1/P19-1377
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1377.pdf