caa a22 4500 467947589 CHVBK 20180323165701.0 cr unu---uuuuu 170328e19870301xx s 000 0 eng 10.1007/BF00058753 doi (NATIONALLICENCE)springer-10.1007/BF00058753 (NATIONALLICENCE)springer-10.1023/A:1022860810097 Learning syntax by automata induction [Elektronische Daten] [Robert Berwick, Sam Pilato] In this paper we propose an explicit computer model for learning natural language syntax based on Angluin's (1982) efficient induction algorithms, using a complete corpus of grammatical example sentences. We use these results to show how inductive inference methods may be applied to learn substantial, coherent subparts of at least one natural language — English — that are not susceptible to the kinds of learning envisioned in linguistic theory. As two concrete case studies, we show how to learn English auxiliary verb sequences (such as could be taking, will have been taking) and the sequences of articles and adjectives that appear before noun phrases (such as the very old big deer). Both systems can be acquired in a computationally feasible amount of time using either positive examples, or, in an incremental mode, with implicit negative examples (examples outside a finite corpus are considered to be negative examples). As far as we know, this is the first computer procedure that learns a full-scale range of noun subclasses and noun phrase structure. The generalizations and the time required for acquisition match our knowledge of child language acquisition for these two cases. More importantly, these results show that just where linguistic theories admit to highly irregular subportions, we can apply efficient automata-theoretic learning algorithms. Since the algorithm works only for fragments of language syntax, we do not believe that it suffices for all of language acquisition. Rather, we would claim that language acquisition is nonuniform and susceptible to a variety of acquisition strategies; this algorithm may be one these. Kluwer Academic Publishers, 1987 Formal inductive inference nationallicence language acquisition nationallicence automata theory nationallicence Berwick Robert MIT Artificial Intelligence Laboratory, 545 Technology Square, 02139, Cambridge, Massachusetts, U.S.A. aut Pilato Sam Brattle Research Corporation, 55 Wheeler Street, 02138, Cambridge, Massachusetts, U.S.A. aut Machine Learning Kluwer Academic Publishers 2/1(1987-03-01), 9-38 0885-6125 2:1<9 1987 2 10994 https://doi.org/10.1007/BF00058753 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00058753 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Berwick Robert MIT Artificial Intelligence Laboratory, 545 Technology Square, 02139, Cambridge, Massachusetts, U.S.A aut NATIONALLICENCE 700 1- Pilato Sam Brattle Research Corporation, 55 Wheeler Street, 02138, Cambridge, Massachusetts, U.S.A aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers 2/1(1987-03-01), 9-38 0885-6125 2:1<9 1987 2 10994 NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1022860810097 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Berwick Robert MIT Artificial Intelligence Laboratory, 545 Technology Square, 02139, Cambridge, Massachusetts, USA aut NATIONALLICENCE 700 1- Pilato Sam Brattle Research Corporation, 55 Wheeler Street, 02138, Cambridge, Massachusetts, USA aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers-Plenum Publishers 2/1(1987-03-01), 9-38 0885-6125 2:1<9 1987 2 10994 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE NATIONALLICENCE NL-springer