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Learning left-to-right and right-to-left iterative languages. (2008)

by Jerey Heinz
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On the role of locality in learning stress patterns

by Jeffrey Heinz - Phonology , 2009
"... This paper presents a previously unnoticed universal property of stress patterns in the world's languages : they are, for small neighbourhoods, neighbourhooddistinct. Neighbourhood-distinctness is a locality condition defined in automatatheoretic terms. This universal is established by examini ..."
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This paper presents a previously unnoticed universal property of stress patterns in the world's languages : they are, for small neighbourhoods, neighbourhooddistinct. Neighbourhood-distinctness is a locality condition defined in automatatheoretic terms. This universal is established by examining stress patterns contained in two typological studies. Strikingly, many logically possible -but unattested -patterns do not have this property. Not only does neighbourhooddistinctness unite the attested patterns in a non-trivial way, it also naturally provides an inductive principle allowing learners to generalise from limited data. A learning algorithm is presented which generalises by failing to distinguish sameneighbourhood environments perceived in the learner's linguistic input -hence learning neighbourhood-distinct patterns -as well as almost every stress pattern in the typology. In this way, this work lends support to the idea that properties of the learner can explain certain properties of the attested typology, an idea not straightforwardly available in optimality-theoretic and Principle and Parameter frameworks.
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...been merged in Machine A in Fig. 11, e.g. states 0 and 4, a di"erent (in this case, incorrect) generalisation would have been made. If all states are merged, the result is a one-state machine, with all transitions self-looping to this state. This machine accepts any string of symbols, indicating a massive overgeneralisation. Thus, choosing which states are to be merged determines the kinds of generalisations that occur (Muggleton 1990, Machine A Machine B 10 ¡ 2s 3s 4s 0 1¡ 2-3s 4s s(a) (b) Figure 11 An example of generalisation by state-merging. (a) Machine A; (b) Machine B. 328 Je!rey Heinz Heinz 2008).18 Amerging strategy is thus a generalisation strategy. It is an inductive principle, in the sense of Popper (1959). There is one key result regarding state-merging: given any canonical acceptorA for any regular language L and a su!cient sample S of L – that is, a sample which exercises every transition in A – there is some way to merge states in the prefix tree of S which returns acceptor A (Angluin 1982). This result does not tell us how to merge the states for a particular acceptor; it just says that such a way exists. Nonetheless, the result is important because it leaves open the possibi...

Learnability

by Jeffrey Heinz, Jason Riggle
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3 Learnability

by Jeffrey Heinz, Ason Riggle
"... How can anything (such as a child or any other computing device) autonMtically acquire (any aspect of) a phonological grammar on the basis of its l~Xp\~ricnct>? This is the fundamental (and unresolved) question of phonological lt:><Hnabilily, and it is essentially independent of whether the ..."
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How can anything (such as a child or any other computing device) autonMtically acquire (any aspect of) a phonological grammar on the basis of its l~Xp\~ricnct&gt;? This is the fundamental (and unresolved) question of phonological lt:&gt;&lt;Hnabilily, and it is essentially independent of whether the grammar is t,1kcn tt&gt; bt. • thl &apos; hvst
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