A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing
نویسندگان
چکیده
Narayanan and Jurafsky (1998) proposed that human language comprehension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension process with Bayesian decision trees. In this paper we extend the Narayanan and Jurafsky model to make further predictions about reading time given the probability of difference parses or interpretations, and test the model against reading time data from a psycholinguistic experiment.
منابع مشابه
A Bottom-Up Parsing Model of Local Coherence Effects
Human sentence processing occurs incrementally. Most models of human processing rely on parsers that always build connected tree structures. But according to the theory of Good Enough parsing (Ferreira & Patson, 2007), humans parse sentences using small chunks of local information, not always forming a globally coherent parse. This difference is apparent in the study of local coherence effects ...
متن کاملThe Integration of Syntax and Semantic Plausibility in a Wide-Coverage Model of Human Sentence Processing
Models of human sentence processing have paid much attention to three key characteristics of the sentence processor: Its robust and accurate processing of unseen input (wide coverage), its immediate, incremental interpretation of partial input and its sensitivity to structural frequencies in previous language experience. In this thesis, we propose a model of human sentence processing that accou...
متن کاملSyntactic Ambiguity Resolution in Sentence Processing: New Evidence from a Morphologically Rich Language
An experimental study dedicated to structurally ambiguous sentences processing was carried out. We analyzed the case of participial construction attachment to a complex noun phrase. In Experiment 1, we used self-paced reading technique which enables to measure reading times of each word in a sentence and error rates in the interpretation of the sentences. Error rates in locally ambiguous senten...
متن کاملSequential vs. Hierarchical Syntactic Models of Human Incremental Sentence Processing
Experimental evidence demonstrates that syntactic structure influences human online sentence processing behavior. Despite this evidence, open questions remain: which type of syntactic structure best explains observed behavior–hierarchical or sequential, and lexicalized or unlexicalized? Recently, Frank and Bod (2011) find that unlexicalized sequential models predict reading times better than un...
متن کاملQuantifying sentence complexity based on eye-tracking measures
Eye-tracking reading times have been attested to reflect cognitive processes underlying sentence comprehension. However, the use of reading times in NLP applications is an underexplored area of research. In this initial work we build an automatic system to assess sentence complexity using automatically predicted eye-tracking reading time measures and demonstrate the efficacy of these reading ti...
متن کامل