Journal
MENTAL LEXICON
Volume 6, Issue 1, Pages 1-33Publisher
JOHN BENJAMINS PUBLISHING CO
DOI: 10.1075/ml.6.1.01elm
Keywords
connectionist models; neural networks; dynamical systems; prediction; event representations; schema; sentence processing
Categories
Funding
- NIH [HD053136, MH60517]
- NSERC [OGP0155704]
- NIH Training Grant [T32-DC000041]
- Kavli Institute of Brain and Mind (UCSD)
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH &HUMAN DEVELOPMENT [R01HD053136] Funding Source: NIH RePORTER
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Although for many years a sharp distinction has been made in language research between rules and words - with primary interest on rules - this distinction is now blurred in many theories. If anything, the focus of attention has shifted in recent years in favor of words. Results from many different areas of language research suggest that the lexicon is representationally rich, that it is the source of much productive behavior, and that lexically specific information plays a critical and early role in the interpretation of grammatical structure. But how much information can or should be placed in the lexicon? This is the question I address here. I review a set of studies whose results indicate that event knowledge plays a significant role in early stages of sentence processing and structural analysis. This poses a conundrum for traditional views of the lexicon. Either the lexicon must be expanded to include factors that do not plausibly seem to belong there; or else virtually all information about word meaning is removed, leaving the lexicon impoverished. I suggest a third alternative, which provides a way to account for lexical knowledge without a mental lexicon.
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