2018 EMNLP EMNLP 2018

Integration complexity and the order of cosisters

Abstract

AbstractThe cost of integrating dependent constituents to their heads is thought to involve the distance between dependent and head and the complexity of the integration (Gibson, 1998). The former has been convincingly addressed by Dependency Distance Minimization (DDM) (cf. Liu et al., 2017). The current study addresses the latter by proposing a novel theory of integration complexity derived from the entropy of the probability distribution of a dependent’s heads. An analysis of Universal Dependency corpora provides empirical evidence regarding the preferred order of isomorphic cosisters—sister constituents of the same syntactic form on the same side of their head—such as the adjectives in pretty blue fish. Integration complexity, alongside DDM, allows for a general theory of constituent order based on integration cost.

🌉 Interdisciplinary Bridge — Interdisciplinary and Machine Learning and Mathematics & Optimization and Natural Language Processing
🧭 Keyword Pioneer — integration complexity
🐣 Hot Topic Early Bird — probability distribution
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio

Authors