A word-by-word human sentence processing complexity metric is presented. This metric formalizes
the intuition that comprehenders have more trouble on words contributing larger amounts of information
about the syntactic structure of the sentence as a whole. The formalization is in terms of the conditional
entropy of grammatical continuations, given the words that have been heard so far. To calculate the predictions of this metric, Wilson and Carroll's (1954) original entropy reduction
idea is extended to infinite languages. This is demonstrated with a mildly context-sensitive language that
includes relative clauses formed on a variety of grammatical relations across the Accessibility Hierarchy
of Keenan and Comrie (1977). Predictions are derived that correlate significantly with repetition accuracy results obtained in a sentence-memory experiment (Keenan & Hawkins, 1987).
%0 Journal Article
%1 Hale2006
%A Hale, John
%D 2006
%J Cognitive Science
%K humanparsing informationtheory sentenceprocessing syntax
%P 643--672
%T Uncertainty About the Rest of the Sentence
%V 30
%X A word-by-word human sentence processing complexity metric is presented. This metric formalizes
the intuition that comprehenders have more trouble on words contributing larger amounts of information
about the syntactic structure of the sentence as a whole. The formalization is in terms of the conditional
entropy of grammatical continuations, given the words that have been heard so far. To calculate the predictions of this metric, Wilson and Carroll's (1954) original entropy reduction
idea is extended to infinite languages. This is demonstrated with a mildly context-sensitive language that
includes relative clauses formed on a variety of grammatical relations across the Accessibility Hierarchy
of Keenan and Comrie (1977). Predictions are derived that correlate significantly with repetition accuracy results obtained in a sentence-memory experiment (Keenan & Hawkins, 1987).
@article{Hale2006,
abstract = {A word-by-word human sentence processing complexity metric is presented. This metric formalizes
the intuition that comprehenders have more trouble on words contributing larger amounts of information
about the syntactic structure of the sentence as a whole. The formalization is in terms of the conditional
entropy of grammatical continuations, given the words that have been heard so far. To calculate the predictions of this metric, Wilson and Carroll's (1954) original entropy reduction
idea is extended to infinite languages. This is demonstrated with a mildly context-sensitive language that
includes relative clauses formed on a variety of grammatical relations across the Accessibility Hierarchy
of Keenan and Comrie (1977). Predictions are derived that correlate significantly with repetition accuracy results obtained in a sentence-memory experiment (Keenan & Hawkins, 1987).
},
added-at = {2007-06-27T19:38:20.000+0200},
author = {Hale, John},
biburl = {https://www.bibsonomy.org/bibtex/29d2b9892b68c5f55c38336e92f2458e2/tmalsburg},
interhash = {293b76d09026af80d2966b488e18dd80},
intrahash = {9d2b9892b68c5f55c38336e92f2458e2},
journal = {Cognitive Science},
keywords = {humanparsing informationtheory sentenceprocessing syntax},
pages = { 643--672},
timestamp = {2007-06-27T19:38:20.000+0200},
title = {Uncertainty About the Rest of the Sentence},
volume = 30,
year = 2006
}