A parallel-process model of on-line inference processing

This paper presents a new model of on-line inference processes during text understanding. The model, called ATLAST, integrates inference processing at the lexical, syntactic, and pragmatic levels of understanding, and is consistent with the results of controlled psychological experiments. ATLAST interprets input text through the interaction of independent but communicating inference processes running in parallel. The focus of this paper is on the initial computer implementation of the ATLAST model, and some observations and issues which arise from that implementation

Having Your Cake and Eating It Too: Autonomy and Interaction in a Model of Sentence Processing

Is the human language understander a collection of modular processes operating with relative autonomy, or is it a single integrated process? This ongoing debate has polarized the language processing community, with two fundamentally different types of model posited, and with each camp concluding that the other is wrong. One camp puts forth a model with separate processors and distinct knowledge sources to explain one body of data, and the other proposes a model with a single processor and a homogeneous, monolithic knowledge source to explain the other body of data. In this paper we argue that a hybrid approach which combines a unified processor with separate knowledge sources provides an explanation of both bodies of data, and we demonstrate the feasibility of this approach with the computational model called COMPERE. We believe that this approach brings the language processing community significantly closer to offering human-like language processing systems.Comment: 7 pages, uses aaai.sty macr

STRATEGIST : a program that models strategy-driven and content-driven inference behavior

In the course of understanding a text, different readers use different inference strategies to guide their choice of interpretations of the events in the text. This is in contrast to previous computer models of understanding, which all use the content-driven inference. The separate strategies are theorized to be composed of the same component inference processes, but of different rules for application of the processes. The use of different strategies occasionally results in different results of new experimental data and a working computer program, called STRATEGIST, that models both strategy-drive and content-driven inference behavior. The rules which make up two of these strategies are presented

Parsing with parallelism : a spreading-activation model of inference processing during text understanding

The past decade of reseatch in Natural Language Processing has universally recognized that, since natural language input is almost always ambiguous with respect to its pragmatic implications, its syntactic parse, and even its lexical analysis (i.e., choice of correct word-sense for an ambiguous word), processing natural language input requires decisions about word meanings, syntactic structure, and pragmatic inferences. The lexical, syntactic, and pragmatic levels of inferencing are not as disparate as they have often been treated in both psychological and artificial intelligence research. In fact, these three levels of analysis interact to form a joint interpretation of text.ATLAST (A Three-level Language Analysis SysTem) is an implemented integration of human language understanding at the lexical, the syntactic, and the pragmatic levels. For psychological validity, ATLAST is based on results of experiments with human subjects. The ATLAST model uses a new architecture which was developed to incorporate three features: spreading activation memory, two-stage syntax, and parallel processing of syntax and semantics. It is also a new framework within which to interpret and tackle unsolved problems through implementation and experimentation

A parallel-process model of on-line inference processing

This paper presents a new model of on-line inference processes during text understanding. The model, called ATLAST, integrates inference processing at the lexical, syntactic, and pragmatic levels of understanding, and is consistent with the results of controlled psychological experiments. ATLAST interprets input text through the interaction of independent but communicating inference processes running in parallel. The focus of this paper is on the initial computer implementation of the ATLAST model, and some observations and issues which arise from that implementation

Recovering from erroneous inferences

Many models of natural language understanding make inference decisions as they process a text, but few models address the issue of how to correct their interpretation when later text reveals that earlier inference decisions are wrong. This paper describes how ATLAST, a marker-passing model of text understanding, approaches this problem. The keys to ATLAST's error recovery capability are a means for remembering the choices it could have made but didn't, and a means for initiating the re-evaluation of those previously rejected choices at the appropriate times. This paper also discusses some of the arguments for and against the psychological validity of a theory of inference retention in human text understanding