• Korean Journal of Cognitive Science
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• v.4 no.2
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• pp.115-143
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• 1994

In this paper,I present an extended two-level model using LR parsing techniques.The LR-based two-level model not only guarantees effcient morphological processing but also achieves a higher degree of descriptive adequacy than Koskenniemi's original model.The two-level model is augmented with an independent morphosyntactic module based on feature-based CF word grammar.By adopting a CF word grammar,our model is capable of dealing with complex words with discontinuous dependencies without having duplicate lexicons.It is shown how LR predictions manifested in the parsing table can help the morphological processor to minimize the dictionary lookup process.

• Journal of KIISE
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• v.43 no.10
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• pp.1079-1084
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• 2016

Single-state parsing automata have a characteristic such that the decision of an action depends only on the current state but not on the parsing history. The memory space and the parsing time of single-state parsing automata are less than the memory space and the parsing time of LR automata. However, the applicable grammar class of single-state parsing automata is less than that of LR automata. This paper provides extended single-state parsing automata, which are applicable to LR grammars. In the prior work, the special state, referred to as the cyclic state was not treated in the construction of single-state parsing automata, and hence, the applicable grammar class was less than LR grammars. The paper solves the problem of cyclic states by processing dynamic information depending on an input string. The proposed method expands the application of grammar class of single-state parsing automata to LR grammars.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.177-183
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• 2011

One of the methodologies for the type-checking of an expression is the attribute grammar. But this attribute grammar is difficult to write because two attributes should be used with the full understanding of parsing steps. So this paper proposes a methodology to construct an expression attribute grammar easily. It shows the possibility to check the type of an expression with only one attribute through a grammar construction method.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.6
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• pp.319-322
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• 2010

Extended PLR grammars are currently the largest subclass of LR grammars whose grammars are transformed into LL grammars satisfying covering property. This paper suggests a simplified covering transformation of the original covering transformation for extended PLR grammars. The proposed covering transformation reduces the original four rule types to the three rule types.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.518-522
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• 2014

A single state parsing automaton has the characteristics of the decision of actions which do not depend on the history of the parsing paths but on the current state. The single state parsing automaton hence has the advantage of the reduced parsing time and a small memory requirement compared to those of the conventional LR automaton. However, currently, the grammar classes generating single state parsing automata have not been known. This paper deals with the grammar classes generating single state parsing automata; in addition, this paper gives the generating method of single state parsing automata of the grammar classes.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.138-141
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• 2006

The subclasses of LR grammars, k-transformable grammars, PLR grammars, and extended PLR grammars are LL covering transformable grammar classes. On other hand, their LL covering transformations are based on different formalisms, and hence, the relationship of the transformations is not obvious. This paper gives a unified model of the LL covering transformations, in which each LL covering transformable grammar class generates its transformation as a specific instance.

• Journal of KIISE
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• v.44 no.3
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• pp.239-245
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• 2017

Probabilistic grammar is used in natural language processing, and the parse result of the grammar has to preserve the probability of the original grammar. As for the representative parsing method, LL parsing and LR parsing, the former preserves the probability information of the original grammar, but the latter does not. A characteristic of a probabilistic parsing automaton has been studied; but, currently, the generating model of probabilistic parsing automata has not been known. The paper provides a model of probabilistic parsing automata based on the single state parsing automata. The generated automaton preserves the probability of the original grammar, so it is not necessary to test whether or not the automaton is probabilistic parsing automaton; defining a probability function for the automaton is not required. Additionally, an efficient automaton can be constructed by choosing an appropriate parameter.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.126-131
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• 2002

The problem whether a given grammar G generates an LL language or not is investigated in respect of LL transformable grammars. The previous work involves a nondeterministic intricated parser construction for the characterization of LL transformable grammars. The method hence does not give the intuitive understanding of the essence of LL languages. This paper suggests a characterization of LL transformable grammars based on grammatical derivations instead of the complicated parser construction. The new characterization contributes to intuitive understanding of the essence of LL languages.

• Korean Journal of Cognitive Science
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• v.6 no.3
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• pp.41-63
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• 1995

We present a LR,bottom-up parsing algorithms for TAG. We will introduce the adjoining rules system to handle the formal properties of TAG and to describe the parsing process more effectively. We will consider the context-free behavior of TAG at the adjoining instant. Then we will present the LR bottom up parsing algorithm for TAG by using this property. The basic idea behind a LR bottom up parsing algorithm can be applied to parsing TAG with other conventional algorithms.

• Journal of Korea Multimedia Society
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• v.14 no.2
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• pp.282-287
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• 2011

The attribute grammar is a grammar for static semantic rules to overcome the difficulties of type checking. But this attribute grammar is difficult to write and understand by its additional rules. A grammar would be preferable if it has simple and small size of an attribute grammar. This paper shows how to decrease the size of an attribute grammar for an expression using LH method. This approach considers the types of an expression. That is, the construction of a semantic relation between the types of the expression can make it possible that an attribute grammar be written with its simpler and smaller size.