• Language and Information
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• v.18 no.2
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• pp.141-168
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• 2014

Recognizing spatial information associated with events expressed in natural language text is essential not only for the interpretation of such events and but also for the understanding of the relations among them. However, spatial information is rarely mentioned as compared to events and the association between event and spatial expressions is also highly implicit in a text. This would make it difficult to automate the extraction of spatial information associated with events from the text. In this paper, we give a linguistic analysis of how spatial expressions are associated with event expressions in a text. We first present issues in annotating narrative texts with reference relations between event and spatial expressions, and then discuss surface-level linguistic characteristics of such relations based on the annotated corpus to give a helpful insight into developing an automated recognition method.

• Lingua Humanitatis
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• v.1 no.2
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• pp.123-139
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• 2001

In the history of linguistics in the world the scholars in India could be regarded as the representative linguists, who had provided the cornerstone of the academic development at linguistics. Without looking into the contents of Indian linguistic theories devised and developed in the past it would be almost impossible to account for the origin of descriptive linguistics and historical linguistics. These linguistics trends became full-fledged in 19 and 20 century and are still accepted by a lot of researchers in order to analyze newly revealed languages and train students only coming up the toddling level of linguistic studies. In this paper I will show how far the influence of Indian linguistics has colored the flow of linguistic growth historically. Especially through the analysis of Panini grammar I will prove the intimate relationship between the Indian linguistic theory and the generative grammar - it is the most active theory at present. The methods that Panini applied to constitute the rules like sutra include lots of information, that also could be discovered at the rules postulated in the generative grammar. One of the common features found at both linguistic theories is the simplicity of rule representation. At the generative grammar a rule has to be established without any redundancy. When certain number of sounds like p, b, m show the same phonological. change relevant to lips (labial in linguistic term) different rules need not to be given for each sound separately. It is better to find a way of putting the sounds together in a rule with grouping the 3 sounds with the shared phonetic feature 'labial'. In Panini grammar the form of a rule was decided based on the simplicity, too. For example, sutra 6.1.77 shows the phonological connection between the vowels i, u r 1 and the semi-vowels y, v, r, 1. However, it does not require to postulate 4 individual rules respectively. Instead a rule in which the vowels and the semi-vowels are involved is suggested, and linguistically the rule make it clear that the more simpler the rules will be the better they can reflect the efficiency of human language acquisition. Although the systems introduced at Panini grammar have some sense of distance from the language education itself we cannot deny the fact that the grammar formulates the a turning point of linguistic development. It is essential for us to think over the grammar from the view point of the modem linguistic theories to understand their root and trunk more thoroughly. It will also help us to predict in which way linguistic tendency will proceed to in future.

• Korean journal of communication and information
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• v.20
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• pp.57-109
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• 2003

The purpose of this study was to investigate narrative structure and meaning structure of pictures In TV reporting. TV reporting consists of linguistic representation and pictural representation with which implicate specific meanings. In the past, the latter has been considered as a kind of added material to the former. But, todays, the latter has been considered more important than the latter in implicating specific meanings. Therefore, the author analysed pictures of TV reporting. Specifically, he selected KBS TV news coverage on '9 11 Disaster of U.S.' with various semiological research methods. Through this study, firstly, It was found that the pictural representation was organized with specific narrative structure applied for linguistic representation. Secondly, its meaning was more important than one of linguistic representation. Thirdly, its styles and structures tended to confine range, abstract and limitation of meanings presented by linguistic representation. Fourthly, images of the pictural representation could be interpreted with narrative structures of linguistic representation.

• Proceedings of the Korean Society for Language and Information Conference
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• 1998.02a
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• pp.10-19
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• 1998

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.643-652
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• 2017

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.359-373
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• 2015

Cognitive reserve(CR) is the ability to optimize or maximize performance through complementary brain networks. CR is relevant to normal aging in cognitive-linguistic abilities. There are few domestic systematic reviews or meta-analyses that analyze the relationships between multiple CR and cognitive-linguistic domains in healthy older people. This meta-analysis included 32 studies published since 2000. In result, education level topped the list, followed by the occupation, cognitively stimulating activities, and the multilingualism. Most studies were related to memory, global cognition, and language. CR had a modest positive association with cognitive-linguistic performance. Multiple domains including memory and language also showed the significant correlations across most measures of CR. This study provides evidence-based information to support cognitive-linguistic ability in normal aging.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.8
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• pp.343-348
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• 2019

In recent years, extractive summarization systems based on end-to-end deep learning models have become popular. These systems do not require human-crafted features and adopt data-driven approaches. However, previous related studies have shown that linguistic analysis features such as part-of-speeches, named entities and word's frequencies are useful for extracting important sentences from a document to generate a summary. In this paper, we propose an extractive summarization system based on deep neural networks using conventional linguistic analysis features. In order to prove the usefulness of the linguistic analysis features, we compare the models with and without those features. The experimental results show that the model with the linguistic analysis features improves the Rouge-2 F1 score by 0.5 points compared to the model without those features.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.698-700
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• 2010

In recently, various types of applications have been developed according the smart phone has become popular. There are many kinds of multimedia, game, information, education, etc according to each user. Most of the linguistic contents in mobile based are concentrated on learning foreign languages like English, Japanese, Chinese compared that the development of contents for Korean education is imperfect. In this paper, so, we implement the linguistic contents for elementary Koran education of foreigners based on android OS to use them by smart phone.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.15-32
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• 2006

The increasing complexity of the socio-economic environments makes it less and less possible for single decision-maker to consider all relevant aspects of problem. Therefore, many organizations employ groups in decision making. In this paper, we present a multiperson decision making method using fuzzy logic with linguistic quantifier when each of group members specifies imprecise judgments possibly both on performance evaluations of alternatives with respect to the multiple criteria and on the criteria. Inexact or vague preferences have appeared in the decision making literatures with a view to relaxing the burdens of preference specifications imposed to the decision-makers and thus taking into account the vagueness of human judgments. Allowing for the types of imprecise judgments in the model, however, makes more difficult a clear selection of alternative(s) that a group wants to make. So, further interactions with the decision-makers may proceed to the extent to compensate for the initial comforts of preference specifications. These interactions may not however guarantee the selection of the best alternative to implement. To circumvent this deadlock situation, we present a procedure for obtaining a satisfying solution by the use of linguistic quantifier guided aggregation which implies fuzzy majority. This is an approach to combine a prescriptive decision method via a mathematical programming and a well-established approximate solution method to aggregate multiple objects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1639-1658
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• 2019

Named entity recognition (NER) is an important technique for improving the performance of data mining and big data analytics. In previous studies, NER systems have been employed to identify named-entities using statistical methods based on prior information or linguistic features; however, such methods are limited in that they are unable to recognize unregistered or unlearned objects. In this paper, a method is proposed to extract objects, such as technologies, theories, or person names, by analyzing the collocation relationship between certain words that simultaneously appear around specific words in the abstracts of academic journals. The method is executed as follows. First, the data is preprocessed using data cleaning and sentence detection to separate the text into single sentences. Then, part-of-speech (POS) tagging is applied to the individual sentences. After this, the appearance and collocation information of the other POS tags is analyzed, excluding the entity candidates, such as nouns. Finally, an entity recognition model is created based on analyzing and classifying the information in the sentences.