Abstract
Language comprehension is usually not understood as a time-critical task. Humans, however, process language on-line, in linear time, and with a single pass over a particular instance of speech or text. This calls for a genuinely cognitive algorithmic approach to simulating language comprehension. A formal conception of language is developed, as well as a model for this conception. An algorithm is presented that generates such a model on-line and from a single pass over a text. The generated model is evaluated qualitatively, by comparing its representations to linguistic segmentations (e.g. syllables, words, sentences). Results show that the model contains synonyms and homonyms as can be found in natural language. This suggests that the algorithm is able to recognize and make consistent use of context–which is crucial to understanding in general. In addition, the underlying algorithm is evaluated against a baseline approach with similar properties. This shows that the generated model is able to capture arbitrarily extended dependencies and therefore to outperform exclusively history-based approaches.
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Notes
- 1.
A reliable indicator for understanding is not the reproduction of text but the ability to tell a story in one’s own words..
- 2.
Elsewhere, histories are also referred to as “contexts” or “windows”.
- 3.
The indices at levels \(l \ge 1 \) are therefore tuples that consist of computational identifiers and the informational resources that they reference in computer memory.
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Wernsdorfer, M. (2018). A Time-Critical Simulation of Language Comprehension. In: Iklé, M., Franz, A., Rzepka, R., Goertzel, B. (eds) Artificial General Intelligence. AGI 2018. Lecture Notes in Computer Science(), vol 10999. Springer, Cham. https://doi.org/10.1007/978-3-319-97676-1_27
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