Interpretation-based processing: a unified theory of semantic sentence comprehension

Abstract

We present interpretation-based processing-a theory of sentence processing that builds a syntactic and a semantic representation for a sentence and assigns an interpretation to the sentence as soon as possible. That interpretation can further participate in comprehension and in lexical processing and is vital for relating the sentence to the prior discourse. Our theory offers a unified account of the processing of literal sentences, metaphoric sentences, and sentences containing semantic illusions. It also explains how text can prime lexical access. We show that word literality is a matter of degree and that the speed and quality of comprehension depend both on how similar words are to their antecedents in the preceding text and how salient the sentence is with respect to the preceding text. Interpretation-based processing also reconciles superficially contradictory findings about the difference in processing times for metaphors and literals Ambiguity is one feature of human language that often frustrates the attempts to automatize its understanding by computers: not only can words have multiple meanings, but sometimes the meaning of a word is not taken at face value. Everyday language is often nonliteral; figurative devices such as irony, indirect request, metaphor, metonymy, or hyperbole are common and are understood easily. Metaphor is a particularly pervasive device: it is a rich source of new words (recent examples include web and couch potato) and, moreover, according to researchers such

An Activation-Based Model of Sentence Processing as Skilled Memory Retrieval

“…It should be possible to extend the model to lower levels of processing to explore the interactions of lexical and syntactic processing; indeed much of the structure is already in place to do so. It should also be possible to extend the model to higher levels of semantic and discourse processing; an integration with the semantic interpretation model of Budiu and Anderson (2004) provides one possible avenue. Given prior ACT-R work on modeling in-dividual differences, it should also be possible to use this model as a basis for accounting for performance differences due to variation in working-memory capacity.…”

Investigating the role of semantic priming in query expression: A framework and two experiments

“…In search over text, the semantics of an item signals its potential value relative to the goal of the search (here item refers to images, text, or other visual objects). Cognitive models instantiate searchers' relevance judgments using prior measures of semantic association between a label for the information goal and the items of interest (Budiu & Anderson, ). In modeling, the strength of the association determines the spread of activation in memory to knowledge about the item (Brumby & Howes, , ; Fu & Pirolli, ; Teo & John, ).…”

CoLiDeS+ Pic: a cognitive model of web-navigation based on semantic information from pictures

“…This hypothesis is supported by Levie and Lentz (1982), who have shown that information is remembered and retrieved better when accompanied by relevant pictures. More precisely, Budiu and Anderson (2004) demonstrated that users can use a rich sentence context to arrive at the meaning of an ambiguous word. Another supporting piece of research is based on construction-integration theory of text comprehension (Kintsch 1998), which explains that the incoming text element, the previously read text, the user goal, and the user background knowledge determine, in this order of priority, which concepts are activated at any given point of time.…”