Download Computational approaches to morphology and syntax by Brian Roark PDF

By Brian Roark

The ebook will entice students and complicated scholars of morphology, syntax, computational linguistics and usual language processing (NLP). It offers a serious and useful consultant to computational strategies for dealing with morphological and syntactic phenomena, displaying how those ideas were used and transformed in perform.

The authors speak about the character and makes use of of syntactic parsers and view the issues and possibilities of parsing algorithms for finite-state, context-free and numerous context-sensitive grammars. They relate ways for describing syntax and morphology to formal mechanisms and algorithms, and current well-motivated ways for augmenting grammars with weights or probabilities.

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Johnson, 1972; Kaplan and Kay, 1994; Mohri and Sproat, 1996), and composition also affords a natural way of implementing prosodic selection, as we shall see. Further examples will be given below, but for now note the obvious case of English plural ‘s’, which is /1z/ after apical fricatives and affricates, /z/ after voiced sounds, and /s/ elsewhere. This rule can be implemented with a transducer T . 7) Our new affix Û adds the suffix and makes the appropriate modifications in one fell swoop. We return below to other cases where affixes have a more drastic effect on their bases, thus further motivating the use of composition.

A WFST computes a regular relation, but in addition it associates each mapping with a weight. For example, in a transducer encoding a rewrite rule system, the weights might represent the probabilities of a particular rule application. 5 A Synopsis of Algorithmic Issues The basic texts on automata theory that we have already cited give algorithms for various finite-state operations including concatenation, Kleene closure, union, intersection, complementation, determinization, and minimization. While these algorithms obviously produce correct results and work fine for small automata and transducers, they are often not efficient enough to handle the very large machines that are typical of serious speech- and language-processing applications.

The basic algorithm for intersection is as follows. Given two automata M = (Q, s , F , , ‰) and M = (Q , s , F , , ‰ ), construct a new automaton M such that: r Its set of states Q = Q × Q is the cross-product of the states of the individual machines. r s = (s , s ) rF =F ×F r = ∩ r ‰ (( p, p ), x) = (q , q ) just in case ‰( p, x) = q is in M and ‰ ( p , x) = q is in M . The basic algorithm for transducer composition is essentially the same, with the difference that with transducers one is matching the output label of one transducer with the input label of the other.

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