• Journal of Internet Computing and Services
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• v.5 no.5
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• pp.119-127
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• 2004

As, the development of language-based programming environment, a study on incremental parsing has become an essential part. The purpose of this paper is to show the more efficient incremental parsing algorithm than earlier one that demands parsing speed and memorizing space too much. This paper suggests the threaded tree construction algorithm not using stack. In addition, to remove the reparsing process, it proposes the algorithm for creation node and construction incremental threaded tree not using stack.

• Journal of the Korean Society for information Management
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• v.1 no.1
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• pp.63-77
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• 1984

The logic programming language, Prolog is quickly gainning the popularity throughout the world. In this paper an approach is described for parsing a natural language, english, using Prolog.

• Journal of KIISE:Software and Applications
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• v.28 no.5
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• pp.448-448
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• 2001

The definitions in Korean dictionary not only describe meanings of title, but also include various semantic information such as hypernymy/hyponymy, meronymy/holonymy, polysemy, homonymy, synonymy, antonymy, and semantic features. This paper purposes to implement a parser as the basic tool to acquire automatically the semantic information from the definitions in Korean dictionary. For this purpose, first we constructed the part-of-speech tagged corpus and the tree tagged corpus from the definitions in Korean dictionary. And then we automatically extracted from the corpora the frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability based on the statistical method. The parser is a kind of the probabilistic chart parser that uses the extracted data. The frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability resolve the noun phrase's structural ambiguity during parsing. The parser uses a grammar factoring, Best-First search, and Viterbi search In order to reduce the number of nodes during parsing and to increase the performance. We experiment with grammar rule's probability, left-to-right parsing, and left-first search. By the experiments, when the parser uses grammar rule's probability and left-first search simultaneously, the result of parsing is most accurate and the recall is 51.74% and the precision is 87.47% on raw corpus.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.743-748
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• 2004

In this paper, we propose a probabilistic Korean parsing model using a syntactic feature, a functional feature, a content feature, and a site feature of a syntactic object for effective syntactic disambiguation. It restricts grammar rules to binary-oriented form to deal with Korean properties such as variable word order and constituent ellipsis. In experiments, we analyze the parsing performance of each feature combination. Experimental results show that the combination of different features is preferred to the combination of similar features. Besides, it is remarkable that the function feature is more useful than the combination of the content feature and the size feature.

• Korean Journal of Cognitive Science
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• v.22 no.4
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• pp.429-447
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• 2011

In this paper, we propose a modified deterministic Korean dependency parser using a projectivity. The modified parser is improved by finding errors, such as cross dependency, from the original parsing results and correcting them according to the projectivity and head-final principles. Our parser also uses parsing history information in addition to rich features, which only a deterministic algorithm can use. Results on the modified parser for ETRI(2005) corpus, that consists of complex sentences, show that our parser outperforms other parsers.

• Journal of KIISE:Software and Applications
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• v.29 no.10
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• pp.755-765
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• 2002

In this paper, we observe various syntactic information for Korean parsing and propose a method to learn constraints and improve the efficiency of a parsing model by using the constraints. The proposed method has the following three characteristics. First, it improves the parsing efficiency since we use constraints that can prevent the parser from generating unsuitable candidates. Second, it is robust on a given Korean sentence because the attributes for the constraints are selected based on the syntactic and lexical idiosyncrasy of Korean. Third, it is easy to acquire constraints automatically from a treebank by using a decision tree learning algorithm. The experimental results show that the parser using acquired constraints can reduce the number of overgenerated candidates up to 1/2~1/3 of candidates and it runs 2~3 times faster than the one without any constraints.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.161-177
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• 2019

In this paper, we study the performance improvement of the answer extraction in Question-Answering system by using sentence dependency parsing result. The Question-Answering (QA) system consists of query analysis, which is a method of analyzing the user's query, and answer extraction, which is a method to extract appropriate answers in the document. And various studies have been conducted on two methods. In order to improve the performance of answer extraction, it is necessary to accurately reflect the grammatical information of sentences. In Korean, because word order structure is free and omission of sentence components is frequent, dependency parsing is a good way to analyze Korean syntax. Therefore, in this study, we improved the performance of the answer extraction by adding the features generated by dependency parsing analysis to the inputs of the answer extraction model (Bidirectional LSTM-CRF). The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. In this study, we compared the performance of the answer extraction model when inputting basic word features generated without the dependency parsing and the performance of the model when inputting the addition of the Eojeol tag feature and dependency graph embedding feature. Since dependency parsing is performed on a basic unit of an Eojeol, which is a component of sentences separated by a space, the tag information of the Eojeol can be obtained as a result of the dependency parsing. The Eojeol tag feature means the tag information of the Eojeol. The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. From the dependency parsing result, a graph is generated from the Eojeol to the node, the dependency between the Eojeol to the edge, and the Eojeol tag to the node label. In this process, an undirected graph is generated or a directed graph is generated according to whether or not the dependency relation direction is considered. To obtain the embedding of the graph, we used Graph2Vec, which is a method of finding the embedding of the graph by the subgraphs constituting a graph. We can specify the maximum path length between nodes in the process of finding subgraphs of a graph. If the maximum path length between nodes is 1, graph embedding is generated only by direct dependency between Eojeol, and graph embedding is generated including indirect dependencies as the maximum path length between nodes becomes larger. In the experiment, the maximum path length between nodes is adjusted differently from 1 to 3 depending on whether direction of dependency is considered or not, and the performance of answer extraction is measured. Experimental results show that both Eojeol tag feature and dependency graph embedding feature improve the performance of answer extraction. In particular, considering the direction of the dependency relation and extracting the dependency graph generated with the maximum path length of 1 in the subgraph extraction process in Graph2Vec as the input of the model, the highest answer extraction performance was shown. As a result of these experiments, we concluded that it is better to take into account the direction of dependence and to consider only the direct connection rather than the indirect dependence between the words. The significance of this study is as follows. First, we improved the performance of answer extraction by adding features using dependency parsing results, taking into account the characteristics of Korean, which is free of word order structure and omission of sentence components. Second, we generated feature of dependency parsing result by learning - based graph embedding method without defining the pattern of dependency between Eojeol. Future research directions are as follows. In this study, the features generated as a result of the dependency parsing are applied only to the answer extraction model in order to grasp the meaning. However, in the future, if the performance is confirmed by applying the features to various natural language processing models such as sentiment analysis or name entity recognition, the validity of the features can be verified more accurately.

• Journal of KIISE:Software and Applications
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• v.34 no.2
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• pp.140-149
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• 2007

This paper presents a statistical model for Korean dependency-based parsing. Although Korean is one of free word order languages, it has the feature of which some word order is preferred to local contexts. Earlier works proposed parsing models using modification lengths due to this property. Our model uses headible path contexts for modification length probabilities. Using a headible path of a dependent it is effective for long distance relation because the large surface context for a dependent are abbreviated as its headible path. By combined with lexical bigram dependency, our probabilistic model achieves 86.9% accuracy in eojoel analysis for KAIST corpus, more improvement especially for long distance dependencies.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.151-159
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• 2014

We discuss the implementation of a compiler for an imperative programming language, using monad in Haskell. This compiler involves a recursive-descent parser conducting nondeterministic parsing, in which backtracking occurs to try with other rules when the application of a production rule fails to parse an input string. Haskell has some strong facilities for parsing. Its algebraic types represent abstract syntax trees in a smooth way, and program codes by monad parsing are so concise that they are highly readable and code size is reduced significantly, comparing with other languages. We also deal with the runtime environment of the assembler and code generation whose target is the Stack-Assembly language based on a stack machine.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.127-137
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• 1996

In this paper, an algorithm for on-line rightward japanese parsing is proposed. The ambiguity in on-line parsing is accumulated until the input is completely finished, since there is not a space between words in the japanese sentence. Thus the algorithm for morphological analysis, based on modified chart, is used in solving it. And the number of searching a word in dirctionary for morphological analysis is also a puzzling problem. The japanese sentence, consist of N characters, has logically its maximum number of N(N+1)/2 searches in the ordinary on-line analysis, which is nearly twice as many as normal off-line. In this paper, the matter is settled through the modification of dictionary format. In experiment, we can accomplish the rate of analysis which is nearly equal to that of off-line parsing. And it becomes clear that the longer a sentence is, the better an analysis efficiency is.