57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 : Proceedings of the conference : July 28 - August 2, 2019, Florence, Italy
Seitenbereich:
3878-3887
Veranstaltungstitel:
57th Annual Meeting of the Association for Computational Linguistics
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.
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