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  • × author_ss:"Hausser, R."
  • × theme_ss:"Computerlinguistik"
  1. Hausser, R.: Grammatical disambiguation : the linear complexity hypothesis for natural language (2020) 0.00
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    Abstract
    DBS uses a strictly time-linear derivation order. Therefore the basic computational complexity degree of DBS is linear time. The only way to increase DBS complexity above linear is repeating ambiguity. In natural language, however, repeating ambiguity is prevented by grammatical disambiguation. A classic example of a grammatical ambiguity is the 'garden path' sentence The horse raced by the barn fell. The continuation horse+raced introduces an ambiguity between horse which raced and horse which was raced, leading to two parallel derivation strands up to The horse raced by the barn. Depending on whether the continuation is interpunctuation or a verb, they are grammatically disambiguated, resulting in unambiguous output. A repeated ambiguity occurs in The man who loves the woman who feeds Lucy who Peter loves., with who serving as subject or as object. These readings are grammatically disambiguated by continuing after who with a verb or a noun.
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    a
  2. Hausser, R.: Language and nonlanguage cognition (2021) 0.00
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    Abstract
    A basic distinction in agent-based data-driven Database Semantics (DBS) is between language and nonlanguage cognition. Language cognition transfers content between agents by means of raw data. Nonlanguage cognition maps between content and raw data inside the focus agent. {\it Recognition} applies a concept type to raw data, resulting in a concept token. In language recognition, the focus agent (hearer) takes raw language-data (surfaces) produced by another agent (speaker) as input, while nonlanguage recognition takes raw nonlanguage-data as input. In either case, the output is a content which is stored in the agent's onboard short term memory. {\it Action} adapts a concept type to a purpose, resulting in a token. In language action, the focus agent (speaker) produces language-dependent surfaces for another agent (hearer), while nonlanguage action produces intentions for a nonlanguage purpose. In either case, the output is raw action data. As long as the procedural implementation of place holder values works properly, it is compatible with the DBS requirement of input-output equivalence between the natural prototype and the artificial reconstruction.

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