Algorithms for computational linguistics by Striegnitz K., et al.

By Striegnitz K., et al.

The identify Computational Linguistics already means that this displine contains comparable items of analysis: ordinary language (NL) is studied and operational equipment are built. either fields are investigated of their personal correct and divide into a variety of issues. This direction introduces a number of NL phenomena including acceptable implementations within the programming language Prolog. the subjects handled are between others Morphology, Finite kingdom recommendations, Syntax, Context unfastened Grammars, Parsing, and Semantics development.

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Instead, we will implement an operation cp that saves an automaton under a new name. 3. Implementing Operations on FSAs scan(cp(a1,b1)). results in the automaton a1 being copied to an automaton b1. : start(a1,1). final(a1,4). trans(a1,1,2,h). trans(a1,2,3,a). ). trans(a1,3,2,h). The cp-operation is used to save the output of other operations. For instance to intersect a1 and a2 and save the result as b2, we call: scan(cp((a1,a2),b2)). 2 Implementation of scan/1 Now we will have a look at the code of scan/1 and its helpers.

Regular Languages 2. pl. (You may see some warnings. ) Use test/2 to test hahu and haho on some laughter of your choice. 3. Think about how to characterize the language that the union-automaton of hahu and haho should accept. 4. Build and test the automaton. First request our program to build the unionautomaton of hahu and haho (indicated by ;) and then save it under the name u (using cp), as follows: scan(cp((hahu;haho), u)). Now you can use test/2 to test u on any input that you think should or shouldn’t be accepted.

So, the general structure of this transducer looks like this: regular stem 1 2 singular irregular stem plural irregular stem 3 4 ε:N ε:N ε:N 5 +:ε 6 8 ε:SG s:PL 9 ε:SG 7 ε:PL What still needs to be specified is how exactly the parts between state 1 and states 2,3, and 4 respectively look like. Here, we need to recognize noun stems and decide whether they are regular or not. We do this be encoding a lexicon in the following way. The transducer part that recognizes cat, for instance, looks like this: c:c a:a t:t And the transducer part mapping mice to mouse can be specified as follows: m:m i:o ε:u c:s e:e 28 Chapter 2.

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