A memory-based model of syntactic analysis: data-oriented parsing

Bonnema, Remko; Scha, Remko; Sima’an, Khalil

doi:10.1080/095281399146481

Cited by 26 publications

(22 citation statements)

References 47 publications

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“…Development of such databases is thus one way to advance this line of investigation. Even in the absence of direct experience with the co-occurrence of certain structures (or certain types of structures), comprehenders might generalize based on some (as of yet unknown) similarity metric across structures (as in exemplar-based accounts of syntactic presentations; Scha, Bod & Sima’An, 1999; Snider, 2008). …”

Section: Discussionmentioning

confidence: 99%

Readers generalize adaptation to newly-encountered dialectal structures to other unfamiliar structures

Fraundorf

Jaeger

2016

Journal of Memory and Language

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Growing evidence suggests that syntactic processing may be guided in part by expectations about the statistics of the input that comprehenders have encountered; however, these statistics and even the syntactic structures themselves vary from situation to situation. Some recent work suggests that readers can adapt to variability in the frequencies of known, but infrequent syntactic structures. But, the relation between adaptation to altered frequencies of familiar structures and learning to process unfamiliar, never-before-seen structures is under-explored. In two self-paced reading experiments, we investigated readers’ adaptation to an unfamiliar structure used in some regional dialects of American English: the needs+past participle structure, such as using The car needs washed to mean The car needs to be washed. Study 1 used a novel Web-based recruitment method to target regions where participants were likely to be familiar (Ohio, western Pennsylvania) or unfamiliar (Colorado) with the needs+past participle structure. Participants unfamiliar with the structure initially read the structure more slowly, but over the course of the experiment came to read it more like the familiar participants. Study 2 further demonstrated that participants who have adapted to needs+past participle generalize this adaptation to a different, but related structure. These results suggest (a) that readers adapt to unfamiliar syntactic structures, (b) that, in doing so, they become more like existing users of those structures, and (c) that they can generalize this other structures that they may also be more likely to encounter. We discuss these results in the context of implicit learning accounts of exposure effects on syntactic processing.

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Section: Discussionmentioning

confidence: 99%

Readers generalize adaptation to newly-encountered dialectal structures to other unfamiliar structures

Fraundorf

Jaeger

2016

Journal of Memory and Language

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“…Typically, they operate on part-of-speech tagged text for which significant syntactic groupings such as noun phrases have been identified prior to the application of the model (Argamon-Engelson, Dagan, & Krymolowski, 1999), or they require a database of already parsed text fragments from which to generalize (Scha, Bod, & Sima'an, 1999).…”

Section: Capturing Structure From Input: Testing Input-driven Approachesmentioning

confidence: 99%

Input-Driven Language Learning

Harrington

Dennis

2002

Stud Second Lang Acquis

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Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.

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“…In this early stage we start from the decision-making process of business component selection to derive the service components and physical elements; this step has been dealt with in our previous work. In the following paragraphs we will determine the candidacy for common service and service aggregation by using use case parsing and modeling techniques [1]. Both political and rational models are constructed in a component-based environment.…”

Section: Introductionmentioning

confidence: 99%

The validation and analysis of component-based metadata integration

2007

Proceedings of the 2007 Euro American Conference on Telematics and Information Systems

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Component decision and validation using political and analytical models are applicable in creating the foundation of component-based enterprise architecture. This research deals with both political and analytical models to take the results from the group decision for further development. We demonstrate how to use analysis and modeling techniques to produce the common component and service aggregation which is the foundation of enterprise integration. Decision input from human factors plays a crucial role in component-based design. We have extended the focus of the methods and techniques to component decision in supporting enterprise integration and ontology interoperability, to follow the nature of business performance. As we believe human interactivity in this early analysis and pre-design stage plays a key role in the integration's success, we have assessed the feasibility and received the consensus of the pre-design stage of industry.

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A memory-based model of syntactic analysis: data-oriented parsing

Cited by 26 publications

References 47 publications

Readers generalize adaptation to newly-encountered dialectal structures to other unfamiliar structures

Readers generalize adaptation to newly-encountered dialectal structures to other unfamiliar structures

Input-Driven Language Learning

The validation and analysis of component-based metadata integration

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