• Genomics & Informatics
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• v.16 no.3
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• pp.75-77
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• 2018

Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining.

• Genomics & Informatics
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• v.16 no.4
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• pp.40.1-40.3
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• 2018

There is a communal need for an annotated corpus consisting of the full texts of biomedical journal articles. In response to community needs, a prototype version of the full-text corpus of Genomics & Informatics, called GNI version 1.0, has recently been published, with 499 annotated full-text articles available as a corpus resource. However, GNI needs to be updated, as the texts were shallow-parsed and annotated with several existing parsers. I list issues associated with upgrading annotations and give an opinion on the methodology for developing the next version of the GNI corpus, based on a semi-automatic strategy for more linguistically rich corpus annotation.

• Proceedings of the IEEK Conference
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• summer
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• pp.103-109
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• 2004

In NLP (Natural Language Processing) tasks, the highest difficulty which computers had to face with, is the built-in ambiguity of Natural Languages. To disambiguate it, formerly, they based on human-devised rules. Building such a complete rule-set is time-consuming and labor-intensive task whilst it doesn't cover all the cases. Besides, when the scale of system increases, it is very difficult to control that rule-set. So, recently, many NLP tasks have changed from rule-based approaches into corpus-based approaches with large annotated corpora. Corpus-based NLP tasks for such popular languages as English, French, etc. have been well studied with satisfactory achievements. In contrast, corpus-based NLP tasks for Vietnamese are at a deadlock due to absence of annotated training data. Furthermore, hand-annotation of even reasonably well-determined features such as part-of-speech (POS) tags has proved to be labor intensive and costly. In this paper, we present our building an annotated English-Vietnamese parallel aligned corpus named EVC to train for Vietnamese-related NLP tasks such as Word Segmentation, POS-tagger, Word Order transfer, Word Sense Disambiguation, English-to-Vietnamese Machine Translation, etc.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.95-100
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• 2003

In this paper, we propose solutions to resolve the problem of many spelling variants and the problem of lack of annotated corpus for training, which are two among the main difficulties in named entity recognition in biomedical domain. To resolve the problem of spotting valiants, we propose a use of edit-distance as a feature for SVM. And we propose a use of virtual examples to automatically expand the annotated corpus to resolve the lack-of-corpus problem. Using virtual examples, the annotated corpus can be extended in a fast, efficient and easy way. The experimental results show that the introduction of edit-distance produces some improvements in protein name recognition performance. And the model, which is trained with the corpus expanded by virtual examples, outperforms the model trained with the original corpus. According to the proposed methods, we finally achieve the performance 75.80 in F-measure(71.89% in precision,80.15% in recall) in the experiment of protein name recognition on GENIA corpus (ver.3.0).

• Asia Pacific Journal of Corpus Research
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• v.2 no.2
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• pp.31-41
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• 2021

This paper makes a quantitative analysis of the diachronic evolution of ancient Chinese vocabulary by constructing and counting a large-scale rough annotated corpus. The texts from Si Ku Quan Shu (a collection of Chinese ancient books) are automatically segmented to obtain ancient Chinese vocabulary with time information, which is used to the statistics on word frequency, standardized type/token ratio and proportion of monosyllabic words and dissyllabic words. Through data analysis, this study has the following four findings. Firstly, the high-frequency words in ancient Chinese are stable to a certain extent. Secondly, there is no obvious dissyllabic trend in ancient Chinese vocabulary. Moreover, the Northern and Southern Dynasties (420-589 AD) and Yuan Dynasty (1271-1368 AD) are probably the two periods with the most abundant vocabulary in ancient Chinese. Finally, the unique words with high frequency in each dynasty are mainly official titles with real power. These findings break away from qualitative methods used in traditional researches on Chinese language history and instead uses quantitative methods to draw macroscopic conclusions from large-scale corpus.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.385-393
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• 2007

Corpora annotated with lots of linguistic information are required to develop robust and statistical natural language processing systems. Building such corpora, however, is an expensive, labor-intensive, and time-consuming work. To help the work, we design and implement an annotation tool for establishing a Korean dependency tree-tagged corpus. Compared with other annotation tools, our tool is characterized by the following features: independence of applications, localization of errors, powerful error checking, instant annotated information sharing, user-friendly. Using our tool, we have annotated 100,904 Korean sentences with dependency structures. The number of annotators is 33, the average annotation time is about 4 minutes per sentence, and the total period of the annotation is 5 months. We are confident that we can have accurate and consistent annotations as well as reduced labor and time.

• Journal of KIISE
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• v.43 no.6
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• pp.636-644
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• 2016

Annotated Corpus is important to understand natural language using machine learning method. In this paper, we propose a new method to automate error reduction of annotated corpora. We use the Ripple-Down Rules(RDR) for reducing errors and Kernel to extend RDR for NLP. We applied our system to the Korean Wikipedia and blog corpus errors to find the annotated corpora error type. Experimental results with various views from the Korean Wikipedia and blog are reported to evaluate the effectiveness and efficiency of our proposed approach. The proposed approach can be used to reduce errors of large corpora.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.162-179
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• 2008

We have developed a tool for annotation of electronic health record (EHR) data. Currently we are in the process of manually annotating a corpus of Norwegian general practitioners' EHRs with mainly linguistic information. The purpose of this project is to attain a linguistically annotated corpus of patient histories from general practice. This corpus will be put to future use in medical language processing and information extraction applications. The paper outlines some of our practical experiences from developing such a corpus and, in particular, the effects of semi-automated annotation. We have also done some preliminary experiments with part-of-speech tagging based on our corpus. The results indicated that relevant training data from the clinical domain gives better results for the tagging task in this domain than training the tagger on a corpus form a more general domain. We are planning to expand the corpus annotations with medical information at a later stage.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.575-585
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• 2018

In this study, the texts of science textbooks of the past 20 years were collected in order to systematically carry out researches on scientific languages and scientific terms that have not been noticed in science education. We have collected all the science textbooks from elementary school to high school in the 6th curriculum, the 7th curriculum, and the 2009 revised curriculum, and constructed a corpus comprising of 132 textbooks in total. Sequentially, a raw corpus, a morphological annotated corpus, and a semantic annotated corpus of science terms, were constructed. The final constructed science textbook corpus was named K-STeC (Korean Science Textbook Corpus). K-STeC is a semantic annotated corpus with semantic classification and classification of scientific terms, together with meta information of bibliographic information such as curriculum, subject, grade, and publisher, location information such as chapter, section, lesson, page, and sentence, and structure information such as main, inquiry activities, reference materials, and titles. Throughout the three-year study period, a new research method was created by integrating the know-how of the three fields of linguistic informatics, computer science and science education, and a large number of experts were put in to produce labor-intensive results. This paper introduces new research methodologies and outcomes by looking at the whole research process and methods, and discusses the possibility of future development of scientific language research and how to use the results.

• Language and Information
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• v.19 no.2
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• pp.19-36
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• 2015

The aim of this paper is to investigate the usage of Korean markers -(n)un and -i/ka in editorial texts focusing on information structure. Noun phrases ending with the markers -(n)un and -i/ka were annotated semi-automatically using a corpus obtained from an online newspaper. Two important factors to determine the choice of markers were examined with the annotated data: referential givenness/newness and position in a sentence. Referential givenness and newness were adopted as indicators of information structure, topic and focus respectively. In addition to quantitative analysis, qualitative analysis was conducted on the selected data. The results suggest that both the marker -(n)un and -i/ka could carry a topic and a focus reading. Sentence position also played a crucial role in determining the marker, and the marker -i/ka was used more frequently in a later position of a sentence than the marker -(n)un.