• Journal of KIISE:Computing Practices and Letters
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• v.13 no.6
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• pp.427-431
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• 2007

In this paper we describe a Korean part-of-speech tagging approach using resolution rules for individual ambiguous word and statistical information. Our tagging approach resolves lexical ambiguities by common rules, rules for individual ambiguous word, and statistical approach. Common rules are ones for idioms and phrases of common use including phrases composed of main and auxiliary verbs. We built resolution rules for each word which has several distinct morphological analysis results to enhance tagging accuracy. Each rule may have morphemes, morphological tags, and/or word senses of not only an ambiguous word itself but also words around it. Statistical approach based on HMM is then applied for ambiguous words which are not resolved by rules. Experiment shows that the part-of-speech tagging approach has high accuracy and broad coverage.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.24-27
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• 2008

As shown in Wikipedia, tagging or cross-linking through major key-words improves the readability of documents. Recently, the Semantic Web rises the importance of social tagging as a key feature of the Web 2.0 and Tag Cloud has emerged as its crucial phenotype. In this paper we provides an efficient method of automated keyword tagging based on controlled term collection, where the computational complexity of O(mN) - if pattern matching algorithm is used - can be reduced to O(mlogN) - if Information Retrieval is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that IR-based tagging speeds up 5.6 times compared with fast pattern matching algorithm.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.282-288
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• 2002

This study was carried out to investigate the optimum tagging method of the abalone juvenile, Haliotis discus hannai in indoor culture system from May 2000 to January2001. Tagging methods were shell drilling, copper-wire tagging at the respiratory pore and nut gluing on the shell. The attachment rates of the shell showed high in the 2- and 3 cm bolt-nut tagged groups, about over $89.5\%$, whereas shell drilling groups on the shell were about $18.5\%$. The internal coating rates tagged with bolt-nut were over $96.6\%$, while those tagged with copper wire were less than $17.1\%$. Growths in the all marked and tagged experimental groups comparing with control groups were not significantly different (p>0,05). Survivals in all tagging groups except shell drilling and nut gluing groups in the 5 cm abalone were over $95\%, Accordingly, all juvenile groups were not affected by the tagging methods in terms of the growth and survival on the abalones. Based on these results, the micro bolt-nut tagging was the most effective method in abalone.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.11-21
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• 2010

To aim at improving performance and reflecting user's needs of retrieval, the number of researches has been actively conducted in recent year as the quantity of information and generation of the web pages exceedingly increase. One of alternative approaches can be a tagging system. It makes users be able to provide a representation of metadata including writings, pictures, and movies etc. called tag and be convenient in use of retrieval of internet resources. Tags similar to keywords play a critical role in maintaining target pages. However, they still needs time consuming labors to annotate tags, which sometimes are found to be a hinderance caused by overuse of tagging. In this paper, we present an automatic tagging scheme for a solution of current tagging system conveying drawbacks and inconveniences. To realize the approach, face recognition-based tagging system on SNS is proposed by building a face area detection procedure, linear-based classification and boosting algorithm. The proposed novel approach of tagging service can increase possibilities that utilized SNS more efficiently. Experimental results and performance analysis are shown as well.

• Smart Media Journal
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• v.11 no.5
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• pp.17-25
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• 2022

Korean part-of-speech taggers decompose a compound morpheme into unit morphemes and attach part-of-speech tags. So, here is a disadvantage that part-of-speech for morphemes are over-classified in detail and complex word types are generated depending on the purpose of the taggers. When using the part-of-speech tagger for keyword extraction in deep learning based language processing, it is not required to decompose compound particles and verb-endings. In this study, the part-of-speech tagging problem is simplified by using a Head-Tail tokenization technique that divides only two types of tokens, a lexical morpheme part and a grammatical morpheme part that the problem of excessively decomposed morpheme was solved. Part-of-speech tagging was attempted with a statistical technique and a deep learning model on the Head-Tail tokenized corpus, and the accuracy of each model was evaluated. Part-of-speech tagging was implemented by TnT tagger, a statistical-based part-of-speech tagger, and Bi-LSTM tagger, a deep learning-based part-of-speech tagger. TnT tagger and Bi-LSTM tagger were trained on the Head-Tail tokenized corpus to measure the part-of-speech tagging accuracy. As a result, it showed that the Bi-LSTM tagger performs part-of-speech tagging with a high accuracy of 99.52% compared to 97.00% for the TnT tagger.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.570-578
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• 2003

Maximum entropy models are promising candidates for natural language modeling. However, there are two major hurdles in applying maximum entropy models to real-life language problems, such as prepositional phrase attachment: feature selection and high computational complexity. In this paper, we propose a maximum entropy boosting model to overcome these limitations and the problem of imbalanced data in natural language resources, and apply it to prepositional phrase (PP) attachment and part-of-speech (POS) tagging. According to the experimental results on Wall Street Journal corpus, the model shows 84.3% of accuracy for PP attachment and 96.78% of accuracy for POS tagging that are close to the state-of-the-art performance of these tasks only with small efforts of modeling.

• Journal of Environmental Science International
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• v.23 no.8
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• pp.1495-1505
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• 2014

The objective of this study was to evaluate survival rate and fish movement (migration) using a tagging approach of passive integrated transponder (PIT) in Juksan Weir, which was constructed as a four major river restoration projects. For this study, survival rates of each fish species and the mobility of fish individuals were analyzed during 2 weeks by the insertion of PIT tags to various fish species in the laboratory. According to tagging tests in the laboratory, the survival rate 37.5% (30 survivals of 80 individuals) after the insertion of PIT tags. The survival rate of Carassius auratus and Hemibarbus labeo was 100% and 80% after the insertion of the tags, respectively, whereas it was only 13.3% for Zacco platypus. In the field experiments of Juksan Weir, 6 species and 157 individuals from 8 species (563 individuals) were detected in the fixed automatic data-logging system, indicating a detection rate of 27.9% in the fishway of Juksan Weir. In the meantime, some species with no or low detection rates in the fixed automatic data-logging system were turn out to be stagnant-type species, which prefer stagnant or standing water to live.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.386-391
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• 2017

In this paper, we propose a system that automatically assigns user's experience-based emotion tags from wearable sensor data collected in real life. Four types of emotional tags are defined considering the user's own emotions and the information which the user sees and listens to. Based on the collected wearable sensor data from multiple sensors, we have trained a machine learning-based tagging system that combines the known auxiliary tools from the existing affective computing research and assigns emotional tags. In order to show the usefulness of this multi-modality-based emotion tagging system, quantitative and qualitative comparison with the existing single-modality-based emotion recognition approach are performed.

• Fisheries and Aquatic Sciences
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• v.10 no.4
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• pp.226-229
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• 2007

Survival, tag retention and tag readability were compared among the control and three treatment groups of soft-shelled turtles, Pelodiscus sinensis Crother, 2000 (mean body $weight{\pm}SD$: $182.6{\pm}13.7\;g$), marked with visible implant fluorescent elastomer (VIFE) tags for 16 months. Mortality 4 to 16 months after tagging was attributed to collection and handling stress rather than to the tagging itself. Tags applied to the web surface between the fourth and fifth dactyl of the hindfoot appeared to have the highest retention rates, while adipose eyelid tagging had high tag readability but a high loss rate. We conclude that in soft-shelled turtles, the most suitable region for VIFE tagging is on the web surface between the fourth and fifth dactyls of the hindfoot.

• Journal of Digital Contents Society
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• v.8 no.3
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• pp.255-262
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• 2007

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs, videos and other content. In this paper, we analyze the structure and basic knowledge of collaborative tagging systems as well as their dynamical aspects. We also present a retrieval system, TagPlus, using synonym tag that is derived from WordNet database. Specifically, TagPlus, a synonym tag based system has users retrieve images from Flickr system. The proposed system show the images tagged by not only the tag that users input but also the synonyms that are synonyms with the tag.