• KIPS Transactions on Software and Data Engineering
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• v.9 no.7
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• pp.221-228
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• 2020

Part-of-Speech (POS) tagged corpus is a collection of electronic text in which each word is annotated with a tag as the corresponding POS and is widely used for various training data for natural language processing. The training data generally assumes that there are no errors, but in reality they include various types of errors, which cause performance degradation of systems trained using the data. To alleviate this problem, we propose a novel method for detecting errors in the existing POS tagged corpus using the classifier of XGBoost and cross-validation as evaluation techniques. We first train a classifier of a POS tagger using the POS-tagged corpus with some errors and then detect errors from the POS-tagged corpus using cross-validation, but the classifier cannot detect errors because there is no training data for detecting POS tagged errors. We thus detect errors by comparing the outputs (probabilities of POS) of the classifier, adjusting hyperparameters. The hyperparameters is estimated by a small scale error-tagged corpus, in which text is sampled from a POS-tagged corpus and which is marked up POS errors by experts. In this paper, we use recall and precision as evaluation metrics which are widely used in information retrieval. We have shown that the proposed method is valid by comparing two distributions of the sample (the error-tagged corpus) and the population (the POS-tagged corpus) because all detected errors cannot be checked. In the near future, we will apply the proposed method to a dependency tree-tagged corpus and a semantic role tagged corpus.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.2
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• pp.227-235
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• 2013

The quality of the part-of-speech (POS) annotation in a corpus plays an important role in developing POS taggers. There, however, are several kinds of errors in Korean POS-tagged corpora like Sejong Corpus. Such errors are likely to be various like annotation errors, spelling errors, insertion and/or deletion of unexpected characters. In this paper, we propose a method for detecting annotation errors using error patterns, and also develop a tool for effectively correcting them. Overall, based on the proposed method, we have hand-corrected annotation errors in Sejong POS Tagged Corpus using the developed tool. As the result, it is faster at least 9 times when compared without using any tools. Therefore we have observed that the proposed method is effective for correcting annotation errors in POS-tagged corpus.

• MALSORI
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• no.67
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• pp.181-193
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• 2008

In this paper, we propose a vocabulary coverage improvement method for embedded continuous speech recognition (CSR) using a part-of-speech (POS) tagged corpus. We investigate 152 POS tags defined in Lancaster-Oslo-Bergen (LOB) corpus and word-POS tag pairs. We derive a new vocabulary through word addition. Words paired with some POS tags have to be included in vocabularies with any size, but the vocabulary inclusion of words paired with other POS tags varies based on the target size of vocabulary. The 152 POS tags are categorized according to whether the word addition is dependent of the size of the vocabulary. Using expert knowledge, we classify POS tags first, and then apply different ways of word addition based on the POS tags paired with the words. The performance of the proposed method is measured in terms of coverage and is compared with those of vocabularies with the same size (5,000 words) derived from frequency lists. The coverage of the proposed method is measured as 95.18% for the test short message service (SMS) text corpus, while those of the conventional vocabularies cover only 93.19% and 91.82% of words appeared in the same SMS text corpus.

• Journal of KIISE
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• v.41 no.9
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• pp.642-651
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• 2014

This paper proposes three probabilistic models for syllable-based Korean morphological analysis, and presents the performance of proposed probabilistic models. Probabilities for the models are acquired from POS-tagged corpus. The result of 10-fold cross-validation experiments shows that 98.3% answer inclusion rate is achieved when trained with Sejong POS-tagged corpus of 10 million eojeols. In our models, POS tags are assigned to each syllable before spelling recovery and morpheme generation, which enables more efficient morphological analysis than the previous probabilistic models where spelling recovery is performed at the first stage. This efficiency gains the speed-up of morphological analysis. Experiments show that morphological analysis is performed at the rate of 147K eojeols per second, which is almost 174 times faster than the previous probabilistic models for Korean morphology.

• MALSORI
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• v.68
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• pp.115-126
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• 2008

In this paper, we propose a vocabulary coverage improvement method for embedded continuous speech recognition (CSR) using knowledgebase. A vocabulary in CSR is normally derived from a word frequency list. Therefore, the vocabulary coverage is dependent on a corpus. In the previous research, we presented an improved way of vocabulary generation using part-of-speech (POS) tagged corpus. We analyzed all words paired with 101 among 152 POS tags and decided on a set of words which have to be included in vocabularies of any size. However, for the other 51 POS tags (e.g. nouns, verbs), the vocabulary inclusion of words paired with such POS tags are still based on word frequency counted on a corpus. In this paper, we propose a corpus independent word inclusion method for noun-, verb-, and named entity(NE)-related POS tags using knowledgebase. For noun-related POS tags, we generate synonym groups and analyze their relative importance using Google search. Then, we categorize verbs by lemma and analyze relative importance of each lemma from a pre-analyzed statistic for verbs. We determine the inclusion order of NEs through Google search. The proposed method shows better coverage for the test short message service (SMS) text corpus.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.7-13
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• 1997

Morphological Analysis of Korean has known to be a very complicated problem. Especially, the degree of part-of-speech(POS) ambiguity is much higher than English. Many researchers have tried to use a hidden Markov model(HMM) to solve the POS tagging problem and showed arround 95% correctness ratio. However, the lack of lexical information involves a hidden Markov model for POS tagging in lots of difficulties in improving the performance. To alleviate the burden, this paper proposes a method for combining multiword units, which are types of lexical information, into a hidden Markov model for POS tagging. This paper also proposes a method for extracting multiword units from POS tagged corpus. In this paper, a multiword unit is defined as a unit which consists of more than one word. We found that these multiword units are the major source of POS tagging errors. Our experiment shows that the error reduction rate of the proposed method is about 13%.

• Journal of the Korea Computer Industry Society
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• v.2 no.12
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• pp.1581-1590
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• 2001

This paper asserted more developmental model of WSD(word sense disambiguation) than J. Hur(2000)'s WSD model. This model suggested an improved statistical homonym disambiguation Model based on Bayes Theory. This paper using semantic information(co-occurrence data) obtained from definitions of part of speech(POS) tagged UMRD-S(Ulsan university Machine Readable Dictionary(Semantic Tagged)). we extracted semantic features in the context as nouns, predicates and adverbs from the definitions in the korean dictionary. In this research, we make an experiment with the accuracy of WSD system about major nine homonym nouns and new seven homonym predicates supplementary. The inner experimental result showed average accuracy of 98.32% with regard to the most Nine homonym nouns and 99.53% for the Seven homonym predicates. An Addition, we save test on Korean Information Base and ETRI's POS tagged corpus. This external experimental result showed average accuracy of 84.42% with regard to the most Nine nouns over unsupervised learning sentences from Korean Information Base and ETRI Corpus, 70.81 ％ accuracy rate for the Seven predicates from Sejong Project phrase part tagging corpus (3.5 million phrases) too.

• Korean Journal of Cognitive Science
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• v.22 no.3
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• pp.327-345
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• 2011

In this paper, a new approach to Korean POS (Part-of-Speech) tagging is proposed. In previous works, a Korean POS tagger was regarded as a post-processor of a morphological analyzer, and as such a tagger was used to determine the most likely morpheme/POS sequence from morphological analysis. In the proposed approach, however, the POS tagger is supposed to generate the most likely morpheme and POS pair sequence directly from the given sentences. 398,632 eojeol POS-tagged corpus and 33,467 eojeol test data are used for training and evaluation, respectively. The proposed approach shows 96.31% of POS tagging accuracy.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1112-1123
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• 2003

WSD(word sense disambiguation) is one of the most difficult problems in Korean information processing. The Bayesian model that used semantic information, extracted from definition corpus(1 million POS-tagged eojeol, Korean dictionary definitions), resulted in accuracy of 72.08% (nouns 78.12%, verbs 62.45%). This paper proposes the statistical WSD model using NPH(New Prior Probability of Homonym sense) and distance weights. We select 46 homonyms(30 nouns, 16 verbs) occurred high frequency in definition corpus, and then we experiment the model on 47,977 contexts from ‘21C Sejong Corpus’(3.5 million POS-tagged eojeol). The WSD model using NPH improves on accuracy to average 1.70% and the one using NPH and distance weights improves to 2.01%.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.549-557
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• 2001

Morphological analysis and POS tagging require a dictionary for the language at hand . In this fashion though it is impossible to analyze a language a dictionary. We also have difficulty if significant portion of the vocabulary is new or unknown . This paper explores the possibility of learning morphology of an agglutinative language. in particular Korean language, without any prior lexical knowledge of the language. We use unsupervised learning in that there is no instructor to guide the outcome of the learner, nor any tagged corpus. Here are the main characteristics of the approach: First. we use only raw corpus without any tags attached or any dictionary. Second, unlike many heuristics that are theoretically ungrounded, this method is based on statistical methods , which are widely accepted. The method is currently applied only to Korean language but since it is essentially language-neutral it can easily be adapted to other agglutinative languages.