Abstract
The outline of a system is described which is designed to infer a grammar from a finite sample of linguistic data (corpus). It is inspired by the research on inductive inference in the sense of Gold(1967). After tagging the corpus, an incremental learning algorithm is used to produce a sequence of grammars which approximates the target grammar of the data provided. In each step, a small set of sentences is selected in a way which reduces the danger of overgeneralization. The sentences selected are analysed by a modified Earley parser which allows to measure the “distance” between the language generated by the actual grammar G and sentences not covered by G. The sentence which minimizes the “inductive leap” for the learner is selected to infer a new grammar. For this sentence several hypotheses for completing its partial structural description are formulated and evaluated. The “best” hypothesis is then used to infer a new grammar. This process is continued until the corpus is completely covered by the grammar.
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© 1993 Springer-Verlag Berlin · Heidelberg
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Schrepp, J. (1993). Acquisition of Syntactical Knowledge from Text. In: Opitz, O., Lausen, B., Klar, R. (eds) Information and Classification. Studies in Classification, Data Analysis and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-50974-2_36
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DOI: https://doi.org/10.1007/978-3-642-50974-2_36
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