Name
Abstract | ||
|---|---|---|
Human syntactic processing shows many signs of taking place within a general-purpose short-term memory. But this kind of memory is known to have a severely constrained storage capacity---possibly constrained to as few as three or four distinct elements. This article describes a model of syntactic processing that operates successfully within these severe constraints, by recognizing constituents in a right-corner transformed representation (a variant of left-corner parsing) and mapping this representation to random variables in a Hierarchic Hidden Markov Model, a factored time-series model which probabilistically models the contents of a bounded memory store over time. Evaluations of the coverage of this model on a large syntactically annotated corpus of English sentences, and the accuracy of a a bounded-memory parsing strategy based on this model, suggest this model may be cognitively plausible. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1162/coli.2010.36.1.36100 | Computational Linguistics |
Keywords | Field | DocType |
human syntactic processing,bounded-memory parsing strategy,hierarchic hidden markov model,probabilistically model,broad-coverage parsing,left-corner parsing,factored time-series model,syntactic processing,general-purpose short-term memory,english sentence,bounded memory store,human-like memory constraint,probabilistic model,random variable,time series model,short term memory | Random variable,Markov model,Computer science,Speech recognition,Artificial intelligence,Natural language processing,Parsing,Hidden Markov model,Syntax,Bounded function | Journal |
Volume | Issue | ISSN |
36 | 1 | 0891-2017 |
Citations | PageRank | References |
27 | 1.16 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| William Schuler | 1 | 125 | 17.78 |
| Samir AbdelRahman | 2 | 88 | 9.54 |
| Tim Miller | 3 | 63 | 8.17 |
| Lane Schwartz | 4 | 209 | 18.01 |