• Journal of English Language & Literature
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• v.60 no.2
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• pp.315-338
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• 2014

In The Great Hunger (1942) Patrick Kavanagh opens an Irish postnationalist discourse. Taking advantage of historical revisionism and postcolonialism, he not only demystifies a romantic nationalist ideology rooted in rural Ireland but also searches for an autonomous literary tradition free of the Irish Literary Revival, supposedly an outcome of a colonial influence. As a farmer-poet, Kavanagh deconstructs in two ways myths of rural areas, to which the Revivalists aspire. Contrary to Revivalism, he reveals that rural Ireland is not an idealized place where national identity arises and individual spirits are restored. It is instead a cruel place where farmer Maguire, deprived of health, wealth, and love, is tortured by hard labor in the field, moral regulations imposed by the Church, and his mother's domestic authority, all of which leave him unmarried until age sixty-five. Kavanagh also challenges the Revivalist tradition, led by W. B. Yeats commonly referred to as the poet of the nation, by indicting its reliance on former colonial authority and its lack of a sense of communal autonomy, both of which are diagnosed as "provincialism" by Kavanagh. Given that modern Irish literature has been strongly colored as nationalistic during the course of anticolonial resistance, Kavanagh's critique of the Revival in The Great Hunger, whose proponents blindly beautify the lives of farmers, runs directly against the grain of the founding ideology of the Irish nation-state. His voice, like that of a whistle-blower, disclosing the harsh realities of rural Ireland, ushers in a "post"-nationalist perspective on nation and national myths in Irish poetics.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• MALSORI
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• no.50
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• pp.51-69
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• 2004

In this paper, we present a one-pass semi-dynamic network decoding framework that inherits both advantages of fast decoding speed from static network decoders and memory efficiency from dynamic network decoders. Our method is based on the novel language model network representation that is essentially of finite state machine (FSM). The static network derived from the language model network [1][2] is partitioned into smaller subnetworks which are static by nature or self-structured. The whole network is dynamically managed so that those subnetworks required for decoding are cached in memory. The network is near-minimized by applying the tail-sharing algorithm. Our decoder is evaluated on the 25k-word Korean broadcast news transcription task. In case of the search network itself, the network is reduced by 73.4% from the tail-sharing algorithm. Compared with the equivalent static network decoder, the semi-dynamic network decoder has increased at most 6% in decoding time while it can be flexibly adapted to the various memory configurations, giving the minimal usage of 37.6% of the complete network size.

• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.388-391
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• 2005

The Protein Structural and Functional Conservation need a common language for data definition. With the help of common language provided by Protein Ontology the high level of sequence and functional conservation can be extended to all organisms with the likelihood that proteins that carry out core biological processes will again be probable orthologues. The structural and functional conservation in these proteins presents both opportunities and challenges. The main opportunity lies in the possibility of automated transfer of protein data annotations from experimentally traceable model organisms to a less traceable organism based on protein sequence similarity. Such information can be used to improve human health or agriculture. The challenge lies in using a common language to transfer protein data annotations among different species of organisms. First step in achieving this huge challenge is producing a structured, precisely defined common vocabulary using Protein Ontology. The Protein Ontology described in this paper covers the sequence, structure and biological roles of Protein Complexes in any organism.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.48-54
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• 2007

A network-based robot [1] is a robot that explores service servers in the network environment for analyzing sensor data and making decision. Since network-based robot architecture was proposed, it's possible to reduce costs of robots. We hope robots would be all around at home environment. Therefore, normal users who are not experts need to be able to control those robots by using easy commands. We developed a scripting language, named CCSLR, to help users and developers who control various robots in ubiquitous environment. We focused on how to design the common language for various robots and how to translate a CCSLR script into a sequence of low-level commands of the target robot. In this paper, we propose scripting methods, with three layers. The CCSLR system reads the profile information from the knowledge representation database. Users don't have to know all about kinematical and mechanical details of a robot. Then again, the CCSLR system will use the profile information to translate the script into separated executable library commands. The CCSLR system manages robot's changing state every time a robot executes a command.

• Review of Korean Society for Internet Information
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• v.14 no.3
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• pp.5-20
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• 2013

ICANN has been governing the Domain Name System(DNS) "technically" since 1998. The architecture is called Internet Governance, and it brings about many different discourses; "What does that govern?", "Who delegate its role to ICANN?"," How could the regime ensure fairness?" etc. This article will analyze on Internet Governance by applying the government approach of Foucault, and try to compare two parts, the 'core' and the 'edge' of Internet Governance for method. Whereas the 'core' of it refers the site that be governed by the formal contract directly, the 'edge' as the rest of it means informal friendly relations with ICANN. The 'core' rule was stemmed from technological community such as IAB or IETF historically. They had invented new world and its population to integrate the technical order as protocol and the semiotic order as language, that be based on new government mode. On the other hand, ".KR" domain, one of the 'edges', has been evolved into more heterogeneous system, through contest and conflict between traditional state and Internet Governance. The governed object of ".KR" domain is situated in the crossing of each other the 'protocol user', the 'language-semiotic user' and the' geographical resident'. Here the 'geographical resident' rule was weird for DNS, so that shows the internal lack of Internet Governance. It needs to move to the concept of 'Hangeul(Korean-language) user' rather than the 'geographical resident'.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.1
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• pp.80-90
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• 2010

Performance management is very popular in business area and seems to be an exciting topic. Despite significant research efforts and myriads of performance metrics, performance management today as a rigorous approach is still in an immature state and metrics are often selected based on intuitive and heuristic approach. In a R&D sector, the difficulty to select the proper performance metrics is even more increasing due to the natural characteristics of R&D such as unique or domain-specific problems. In this paper, we present a way of presenting R&D performance framework using ontology language. Based on this, the specific metrics can be derived by reusing or inheriting the context in the framework. The proposed ontological framework is formalized using OWL(Ontology Web Language) and metrics selection rules satisfying the characteristics of R&D are represented in SWRL(Semantic Web Rule Language). Actual metrics selection procedure is carried out using JESS rule engine, a plug-in to Prot$\acute{e}$g$\acute{e}$, and illustrated with an example, incorporating a prevalent R&D performance model : TVP(Technology Value Pyramid).

• Cross-Cultural Studies
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• v.31
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• pp.395-414
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• 2013

EPS-TOPIK is a test of Korean proficiency which is enforced at the level of Korean government for the selection of competent foreign workers. Therefore, it must ensure validity and reliability, and practicability as a language assessment tool. For seeking a way of developing EPS-TOPIK, in this paper, the current state of EPS-TOPIK was investigated based upon the materials published by the Korean government, and the questions of EPS-TOPIK were analysed. Together with this, newspaper articles both in Korea and in foreign countries were also analyzed, and the directors of the EPS center abroad were interviewed. According to the survey results, as expected, the local environment of Korean education is poor in a number of ways to improve the Korean communication skills of foreign workers, as well as to prepare the EPS-TOPIK. To improve the efficiency of the EPS-TOPIK and to enhance the Korean communication skills of foreign workers, the Korean language institutions including the King Sejong Institute, which are in charge of the Korean language education should closely cooperate with the Human Resources Development Service of Korea, which is responsible for all of the influx of foreign workers.