• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.571-579
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• 2009

A left-comer and look-ahead chart parsing algorithm suppresses the generation of meaningless intermediate structures, and thus, gains parsing speed-ups. However, the algorithm requires additional costs to maintain left-comer and look-ahead information throughout the parsing process. Albeit the additional costs, previous research shows that significant parsing speed-ups have been achieved for large-sized context-free grammars. In this paper, we perform similar experiments with a small-sized grammar. We still get parsing speed-ups, but relatively low. We also find that left-comer information has rather negative effects on parsing speed-ups.

• Korean Journal of Cognitive Science
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• v.15 no.1
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• pp.25-33
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• 2004

This paper discusses two problems with LR parsing with regard to constructing parsing tables with feature-based grammars. First, we show that traditional LR parsing methods suffer from nontermination and nondeterminism problems when they are applied to feature-based grammars. We then present an LR method for feature-based grammars that avoids both nontermination and nondetermisim by making use of partial information of a feature structure. Second, we describe the problem of adapting LR parsing to feature-based grammars with schematic rules (i.e., rules that do not contain enough information to construct parsing tables). To remedy this problem, we propose a rule inference algorithm which instantiates underspecified rules into more specified ones containing enough information.

• Journal of KIISE
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• v.44 no.8
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• pp.822-831
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• 2017

In this paper, we propose a Korean dependency parsing model using multi-task learning based pointer networks. Multi-task learning is a method that can be used to improve the performance by learning two or more problems at the same time. In this paper, we perform dependency parsing by using pointer networks based on this method and simultaneously obtaining the dependency relation and dependency label information of the words. We define five input criteria to perform pointer networks based on multi-task learning of morpheme in dependency parsing of a word. We apply a fine-tuning method to further improve the performance of the dependency parsing proposed in this paper. The results of our experiment show that the proposed model has better UAS 91.79% and LAS 89.48% than conventional Korean dependency parsing.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.93-102
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• 2022

Dependency parsing is a decision problem of the syntactic relation between words in a sentence. Recently, deep learning models are used for dependency parsing based on the word representations in a continuous vector space. However, it causes a mislabeled tagging problem for the proper nouns that rarely appear in the training corpus because it is difficult to express out-of-vocabulary (OOV) words in a continuous vector space. To solve the OOV problem in dependency parsing, we explored the proper noun embedding method according to the embedding unit. Before representing words in a continuous vector space, we replace the proper nouns with a special token and train them for the contextual features by using the multi-layer bidirectional LSTM. Two models of the syllable-based and morpheme-based unit are proposed for proper noun embedding and the performance of the dependency parsing is more improved in the ensemble model than each syllable and morpheme embedding model. The experimental results showed that our ensemble model improved 1.69%p in UAS and 2.17%p in LAS than the same arc-eager approach-based Malt parser.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.524-543
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• 2022

Correct facade image parsing is essential to the semantic understanding of outdoor scenes. Unfortunately, there are often various occlusions in front of buildings, which fails many existing methods. In this paper, we propose an end-to-end deep network for facade parsing with occlusions. The network learns to decompose an input image into visible and invisible parts by occlusion reasoning. Then, a context aggregation module is proposed to collect nonlocal cues for semantic segmentation of the visible part. In addition, considering the regularity of man-made buildings, a repetitive pattern completion branch is designed to infer the contents in the invisible regions by referring to the visible part. Finally, the parsing map of the input facade image is generated by fusing the results of the visible and invisible results. Experiments on both synthetic and real datasets demonstrate that the proposed method outperforms state-of-the-art methods in parsing facades with occlusions. Moreover, we applied our method in applications of image inpainting and 3D semantic modeling.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.845-850
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• 2010

Incremental parsing has been developed to reuse the parse result of the original string during the parsing of a new string. The previous incremental LL(1) parsing methods precomputed the reusable point information before parsing and used it during parsing. This paper proposes an efficient reusable point computation by factoring the common part of the computation. The common symbol storing method and the distance storing method were previously suggested to find the reusable point, and by combining the methods, this paper gives the storing method of the distance to common symbols. Based on it, an efficient incremental LL(1) parser is constructed.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.274-278
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• 2006

An upgraded algorithm that in proves the performance of existing interfacing software for parsing HL7 messages is introduced. It incorporates stack operations on objects to guarantee segment order while parsing messages. This object-oriented design greatly facilitates the complicated process of validating, parsing, and creating HL7 messages in the clinical setting. The new interface engine can manage all HL7 messages corresponding to admission and registration, discharge and transfer, laboratory results, clinical images, and clinical reports. The international version of this engine, currently under development, will be tested in Asian countries using standard character code such as Unicode (ISO 10646).

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2113-2123
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• 1999

The partial free order feature of Korean makes grammar size represented by CFG too big and that's why grammar has to contain all the ordered words. There are some problems to parse spoken language, because spontaneous spoken language has special features such as meaningless words, repetitious speech, etc. So, in this paper, we define 'Core-Concept' as the necessary element for parsing and we describe grammar only using Core-Concept. And we can prevent grammar from becoming very large and reduce an additional parsing burden as we select. Core-Concept described in grammar as parsing element. Through this strategy, we present that the simplified grammar can give us more efficient method to get right results. Experiments show that our parsing strategy has an average of 98% or over success rate in correct parsing results.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.21-28
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• 2016

The performance of an English-Korean machine translation system depends heavily on its English parser. The parser in this paper is a part of the rule-based English-Korean MT system, which includes many syntactic rules and performs the chart-based parsing. The parser generates too many structures due to many syntactic rules, so much time and memory are required. The rule-based parser has difficulty in analyzing and translating the long sentences including the commas because they cause high parsing complexity. In this paper, we propose the 3-phase parsing method with sentence segmentation to efficiently translate the long sentences appearing in usual. Each phase of the syntactic analysis applies its own independent syntactic rules in order to reduce parsing complexity. For the purpose, we classify the syntactic rules into 3 classes and design the 3-phase parsing algorithm. Especially, the syntactic rules in the 3rd class are for the sentence structures composed with commas. We present the automatic rule acquisition method for 3rd class rules from the syntactic analysis of the corpus, with which we aim to continuously improve the coverage of the parsing. The experimental results shows that the proposed 3-phase parsing method is superior to the prior parsing method using only intra-sentence segmentation in terms of the parsing speed/memory efficiency with keeping the translation quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2469-2475
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• 2015

A structured XML has been widely used to present services on various Web-services. The XML is also used for digital documents and digital signatures and for the representation of multimedia files in email systems. The XML document should be firstly parsed to access elements in the XML. The parsing is the most compute-instensive task in the use of XML documents. Most of the previous work has focused on hardware based XML parsers in order to improve parsing performance, while a little work has studied parsing techniques. We present the high performance parsing technique which can be used all of XML parsers and design hardware based XML parser using an FPGA. The proposed parsing technique uses element analyzers instead of the state machine and performs multibyte-based element matching. As a result, our parsing technique can reduce the number of clock cycles per byte(CPB) and does not need to require any preprocessing, such as loading XML data into memory. Compared to other parsers, our parser acheives 1.33~1.82 times improvement in the system performance. Therefore, the proposed parsing technique can process XML documents in real time and is suitable for applying to all of XML parsers.