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The representations are developed automatically by all networks while they are learning their processing tasks. The resulting representations reflect the regularities in the subtasks, which facilitates robust processing in the face of noise and damage, supports improved generalization, and provides expectations about possible contexts. The lexicon can be extended by cloning new instances of the items, that is, by generating a number of items with known processing properties and distinct identities. This technique combinatorially increases the processing power of the system. The recurrent FGREP module, together with a central lexicon, is used as a basic building block in modeling higher level natural language tasks. A single module is used to form case\u2010role representations of sentences from word\u2010by\u2010word sequential natural language input. A hierarchical organization of four recurrent FGREP modules (the DISPAR system) is trained to produce fully expanded paraphrases of script\u2010based stories, where unmentioned events and role fillers are inferred.<\/jats:p>","DOI":"10.1207\/s15516709cog1503_2","type":"journal-article","created":{"date-parts":[[2005,7,5]],"date-time":"2005-07-05T19:33:44Z","timestamp":1120592024000},"page":"343-399","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":92,"title":["Natural Language Processing With Modular Pdp Networks and Distributed Lexicon"],"prefix":"10.1111","volume":"15","author":[{"given":"Risto","family":"Miikkulainen","sequence":"first","affiliation":[]},{"given":"Michael G.","family":"Dyer","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2010,2,11]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/0950-7051(90)90003-Z"},{"key":"e_1_2_1_3_1","unstructured":"Ballard D.H.(1987).Modular learning in neural networks. 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