Aesun Yoon

Semantic representation of Korean numeral classifier and its ontology building for HLT applications

The complexity of Korean numeral classifiers demands semantic as well as computational approaches that employ natural language processing (NLP) techniques. The classifier is a universal linguistic device, having the two functions of quantifying and classifying nouns in noun phrase constructions. Many linguistic studies have focused on the fact that numeral classifiers afford decisive clues to categorizing nouns. However, few studies have dealt with the semantic categorization of classifiers and their semantic relations to the nouns they quantify and categorize in building ontologies. In this article, we propose the semantic recategorization of the Korean numeral classifiers in the context of classifier ontology based on large corpora and KorLex Noun 1.5 (Korean wordnet; Korean Lexical Semantic Network), considering its high applicability in the NLP domain. In particular, the classifier can be effectively used to predict the semantic characteristics of nouns and to process them appropriately in NLP. The major challenge is to make such semantic classification and the attendant NLP techniques efficient. Accordingly, a Korean numeral classifier ontology (KorLexClas 1.0), including semantic hierarchies and relations to nouns, was constructed.

An automatic transcription system for Arabic numerals in Korean

We have proposed Auto-TAN, an automatic transcription system of Arabic numerals into Korean alphabetic letters using linguistic rules and clues. Few previous studies have previously discussed the problems in transcribing Arabic numerals into Korean text. We have suggested detailed NRF (number reading formula) paradigms, analyzed the structure of NUMEs (numerical expressions) and components in a larger scope, and investigated compatibilities and selection rules among those components. Based on these linguistic features, 13 stereotyped patterns, 16 rules and 63 clues determining NRF types are formulated for Auto-TAN. This system works modularly in 5 steps. The pilot test was conducted with a test suite which contains 56782 NUMEs. Encouraging results of 84.8% and 10.5% accuracy were obtained for unique transcription and multiple transcriptions, respectively.

Building korean classifier ontology based on korean wordnet

Being commonly used in most languages, the classifier must be reexamined using semantic classes from ontology However, few studies have dealt with the semantic categorization of classifiers and their semantic relations to nouns, which they quantify and characterize in building ontology In this paper, we propose the semantic recategorization of numeral classifiers in Korean and present the construction of a classifier ontology based on large corpora and KorLex 1.5 (Korean WordNet) As a result, a Korean classifier ontology containing semantic hierarchies and the relations of classifiers was constructed This is the first Korean classifier ontology, and its size is appropriate for natural language processing In addition, each of the individual classifiers has a connection to nouns or noun classes that are quantified by the classifiers.

Disambiguation based on wordnet for transliteration of arabic numerals for korean TTS

Transliteration of Arabic numerals is not easily resolved. Arabic numerals occur frequently in scientific and informative texts and deliver significant meanings. Since readings of Arabic numerals depend largely on their context, generating accurate pronunciation of Arabic numerals is one of the critical criteria in evaluating TTS systems. In this paper, (1) contextual, pattern, and arithmetic features are extracted from a transliterated corpus; (2) ambiguities of homographic classifiers are resolved based on the semantic relations in KorLex1.0 (Korean Lexico-Semantic Network); (3) a classification model for accurate and efficient transliteration of Arabic numerals is proposed in order to improve Korean TTS systems. The proposed model yields 97.3% accuracy, which is 9.5% higher than that of a customized Korean TTS system.

Statistical Context-Sensitive Spelling Correction Using Typing Error Rate

Error words that appear in Korean texts can be largely categorized into non-word spelling errors and context-sensitive spelling errors. Of the two, context-sensitive spelling errors are shown only when considering the meaning of the word in the given context and its syntactic relation, and they are the most difficult to correct among spelling errors. Context-sensitive spelling errors can be categorized into homophone errors, typographical errors, grammatical errors, and cross-word boundary errors. To correct context-sensitive spelling errors that occur due to typographical errors, this study proposes a statistical context-sensitive spelling check using confusion sets. With confusion sets created in advance, we can find and correct context-sensitive spelling errors using reliability based on the conditional probability between each word of the confusion sets and the context as well as the typing error rate. As a result of applying the proposed method, all 5 confusion sets showed higher precision and recall than the baseline (precision 80%, recall 80%).

Improving Recall for Context-Sensitive Spelling Correction Rules using Conditional Probability Model with Dynamic Window Sizes

The types of errors corrected by a Korean spelling and grammar checker can be classified into isolated-term spelling errors and context-sensitive spelling errors (CSSE). CSSEs are difficult to detect and to correct, since they are correct words when examined alone. Thus, they can be corrected only by considering the semantic and syntactic relations to their context. CSSEs, which are frequently made even by expert wiriters, significantly affect the reliability of spelling and grammar checkers. An existing Korean spelling and grammar checker developed by P University (KSGC 4.5) adopts hand-made correction rules for correcting CSSEs. The KSGC 4.5 is designed to obtain very high precision, which results in an extremely low recall. Our overall goal of previous works was to improve the recall without considerably lowering the precision, by generalizing CSSE correction rules that mainly depend on linguistic knowledge. A variety of rule-based methods has been proposed in previous works, and the best performance showed 95.19% of average precision and 37.56% of recall. This study thus proposes a statistics based method using a conditional probability model with dynamic window sizes. in order to further improve the recall. The proposed method obtained 97.23% of average precision and 50.50% of recall.

Transliteration system for Arabic-Numeral Expressions using decision tree for intelligent Korean TTS

Though there has been much work on TTS technologies and several TTS systems have customized for Korean, current TTS systems output many errors in transliterating non-alphabetic symbols such as Arabic numerals and text symbols. Arabic Numeral Expressions (ANEs) show a high occurrence-frequency and deliver significant senses, especially in scientific or informative documents and texts. This paper proposes TAN (Transliteration system for Arabic-Numeral expressions) which can efficiently disambiguate the meaning and reading of Arabic Numeral Expressions in texts by using a decision tree. For the purpose of analyzing and learning data, three phases of learning elements were suggested: patterns of Arabic numerals combined with text symbols, contextual features and heuristic information were classified according to the senses and sounds of ANEs. Our corpus was made up of news articles issued from January 1st, 2000 to December 31SI, 2001 from 10 major newspapers in Korea. By learning the three phases of learning elements, the system shows 97.52% and 97.29% accuracies for the training set and the test set, respectively. This result shows that the accuracy of our system is 9.72% higher than that of a current TTS system for Korean.

Potential Governing Relationship and a Korean Grammar Checker Using Partial Parsing

This paper deals with a better method to treat the various linguistic errors and ambiguities that we encounter when analyzing Korean text automatically. A natural language understanding system and the full-sentence analysis would provide a better way to resolve such problems. But the practical application of natural language understanding is still far from being achieved and full sentence analysis in the current state is not only difficult to implement but also time consuming. For those reasons a Korean Grammar Checker using the partial parsing method and the conception of potential governing relationship is implemented. The paper improves the knowledge base of disambiguation rules while trying to reduce them with the result of the linguistic analysis. The extended lexical disambiguation rules and the parsing method based on the asymmetric relation that we propose thus guarantee the accuracy and efficiency of the Grammar Checker.

Semantic Feature-Based Korean Classifier Module for MT Systems

In this paper we propose a Korean numeral classifier system (KCL-SYS), a sub-module for generating the relation(s) between a Korean numeral classifier and its co-occurring noun(s), using semantic features and the LUB (Least Upper Bound) based on semantic hierarchies extracted from KorLex1Noun 1.5, to be applied to preprocessing in MT (MachineTranslation) systems. The ratio of recall in matching classifier-noun(s) was 79.51%, and the precision was 99.52%.

Grapheme-to-Phoneme Conversion of Arabic Numeral Expressions for Embedded TTS Systems

Despite the increasing need for accuracy, current text-to-speech (TTS) systems are still poor at generating the correct pronunciation of Arabic numerals due to their high ambiguity and various interpretations. In this paper, we propose a mini-transliteration system for Arabic-numeral expressions, which can efficiently and correctly convert Arabic numeral expressions found in Korean text into phonemes for embedded TTS systems. For the purpose of building grapheme-to-phoneme rules, we deduced the components of ANEs, and investigated their pattern and arithmetic features based on the analyzed corpus. A word sense disambiguation based on lexical hierarchies in KorLex 1.0 was developed to resolve ambiguities caused by the homographic components of the ANEs. Our system minimized the amount of memory used by 1) separating the morphological analysis module from the transliteration system, 2) compacting the lexicon size, and 3) removing named entities. It reduced the process time dramatically without any serious loss of accuracy, and showed an accuracy of 97.2%-98.3%, which was 21.4%-22.5% higher than that of the baseline, and 5.5%-19.5% higher than current commercial Korean TTS systems