• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.313-316
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• 2002

본 연구에서는 기능적 자기공명 영상법(functional magnetic resonance imaging)을 이용하여, 한국어, 일어, 프랑스어, 영어 등 여러 언어를 구사할 수 있는 다중언어화자들을 대상으로 각 언어에 따른 대뇌 언어처리 과정을 알아보고, 그 처리과정이 해당언어의 유창성, 습득시기에 따라 어떻게 달라지는지를 알아보았다. 실험 결과, 언어처리에 있어 핵심적인 역할을 하는 것으로 보고되는 Broca 영역은 언어의 이해와 산출 과정에 모두 관계된 것으로 보이며, 언어의 산출과정에는 언어의 이해과정에 관계되는 영역외에 조음과정에 따른 영역의 활성화가 보고되었다. 또한 언어습득시기와 유창성에 따른 각 언어의 활성화를 살펴보면, 유창성이 높을수록 대뇌 활성화는 줄어들며, 유창성이 낮은 언어조건에서는 언어처리 영역의 활성화 수준이 높아지며 또한 우반구 및 전전두회(prefrontal gyrus)의 활성화가 높아지는 것이 보인다.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.131-135
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• 2005

Previous studies on the bilinguals' lexical selection have suggested some evidence in favor of language-specific hypothesis. The purpose of this study was to see whether Korean-English bilinguals' semantic systems of Korean and English are shared or separated between the two languages. In a series of picture-word interference tasks, participants were to name the pictures in Korean or in English with distractor words printed either in Korean or English. The distractor words were either semantically identical, related, unrelated to the picture, or nonexistant. The response time of naming was facilitated when distractor words were semantically identical for both same-(Naming pictures in English/korean with English/Korean distractor words) and different-language pairs(Naming pictures in English with Korean distractor words and vice versa). But this facilitation effect was stronger when naming was produced in their native language, which in this case was Korean. Also, inhibitory effect was shown when the picture and its distractor word were semantically related in both same- and different-language paired conditions. These results show that bilinguals'two lexicons compete to some extent when selecting the target word. In this viewpoint, it can be concluded that the lexicons of either languages may not be entirely but partly overlapping in bilinguals.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.249-254
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• 2010

We propose an automatic spam filter for e-mail data using Support Vector Machines(SVM). We use a lexical form of a word and its part of speech(POS) tags as features and select features by chi square statistics. We represent each feature by TF(text frequency), TF-IDF, and binary weight for experiments. After training SVM with the selected features, SVM classifies each e-mail as spam or not. In experiment, the selected features improve the performance of our system and we acquired overall 98.9% of accuracy with TREC05-p1 spam corpus.

• Journal of KIISE
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• v.43 no.12
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• pp.1376-1384
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• 2016

Computers require analytic and processing capability for all possibilities of human expression in order to process sentences like human beings. Linguistic information processing thus forms the initial basis. When analyzing a sentence syntactically, it is necessary to divide the sentence into components, find obligatory arguments focusing on predicates, identify the sentence core, and understand semantic relations between the arguments and predicates. In this study, the method applied a case frame dictionary based on The Korean Standard Dictionary of The National Institute of the Korean Language; in addition, we used a CRF Model that constructed subcategorization of predicates as featured in Korean Lexical Semantic Network (UWordMap) for semantic role labeling. Automatically tagged semantic roles based on the CRF model, which established the information of words, predicates, the case-frame dictionary and hypernyms of words as features, were used. This method demonstrated higher performance in comparison with the existing method, with accuracy rate of 83.13% as compared to 81.2%, respectively.

• Korean Journal of Cognitive Science
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• v.23 no.3
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• pp.349-366
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• 2012

Two experiments were conducted to examine the processing of Korean compounds in relation to saisios. Saisios is a letter interposed between constituents when a phonological change takes place on the onset of the first syllable of the second constituent. This saisios rule is often violated by writers, resulting in many words having two spellings: one with saisios and the other without saisios. Among two spellings, some words are more familiar with saisios, some are usually spelled without saisios, and some are balanced. In Experiment 1 using the go/no-go lexical decision task, participants were asked to judge compounds with/without saisios. Saisios-dominant words (나뭇잎 > 나무잎) were responded faster when they appeared with saisios, whereas the opposite was true for words that usually appear without saisios (북엇국 < 북어국). In experiment 2, we presented participants compound words that were balanced on saisios. The results showed that words without saisios were responded faster than words with saisios. To summarize, the results of Experiment 1 and 2 were consistent with the APPLE model. Some problems related to the saisios rule were discussed in terms of reading process.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.129-139
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• 1994

This paper presents a morphological analysis method for the Korean language. The characteristics and adjacency information of the words can be obtained from sentences in a large corpus. Generally a word can be analyzed to a result by applying the adjacency attributes and rules. However, we have to choose one from the several results for the ambiguous words. The collected morpheme's adjacency attributes and relations with neighbor words are recorded in a well designed dictionaries. With this information, abbreviated words as well as ambiguous words can be almost analyzed successfully. Efficiency of morphological analyzer depends on the information in the dictionaries. A morpheme dictionary and a phrase dictionary have been designed with lexical database, and necessary information extracted from the corpus is stored in the dictionaries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.3819-3828
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• 2015

The present study discusses some of important issues in the word recognition such as the roles of the form(orthographic & phonologic) and semantic information by investigating the processes of Korean heteronym. The priming paradigm has been applied to see whether or not there would be facilitatory effect from form and/or semantic information. In experiment 1, orthographically-related or phonologically-related prime stimuli were presented and a lexical decision task for Korean heteronym was conducted. The same procedure was applied for the experiment 2, except the prime stimulus which was semantically-related. The results showed that orthographic and phonologic information did not influence the processing of the heteronym while semantic information facilitated its processing, suggesting that the semantic information plays an important role in the processes of the Korean heteronym.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.41-49
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• 2019

We are to build an unsupervised machine learning-based language model which can estimate the amount of information that are in need to process words consisting of subword-level morphemes and syllables. We are then to investigate whether the reading times of words reflecting their morphemic and syllabic structures are predicted by an information-theoretic measure such as surprisal. Specifically, the proposed Morfessor-based unsupervised machine learning model is first to be trained on the large dataset of sentences on Sejong Corpus and is then to be applied to estimate the information-theoretic measure on each word in the test data of Korean words. The reading times of the words in the test data are to be recruited from Korean Lexicon Project (KLP) Database. A comparison between the information-theoretic measures of the words in point and the corresponding reading times by using a linear mixed effect model reveals a reliable correlation between surprisal and reading time. We conclude that surprisal is positively related to the processing effort (i.e. reading time), confirming the surprisal hypothesis.

• ETRI Journal
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• v.44 no.4
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• pp.573-587
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• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.147-154
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• 2018

Natural language processing (NLP) is an emerging research area in which we study how machines can be used to perceive and alter the text written in natural languages. We can perform different tasks on natural languages by analyzing them through various annotational tasks like parsing, chunking, part-of-speech tagging and lexical analysis etc. These annotational tasks depend on morphological structure of a particular natural language. The focus of this work is part-of-speech tagging (POS tagging) on Hindi language. Part-of-speech tagging also known as grammatical tagging is a process of assigning different grammatical categories to each word of a given text. These grammatical categories can be noun, verb, time, date, number etc. Hindi is the most widely used and official language of India. It is also among the top five most spoken languages of the world. For English and other languages, a diverse range of POS taggers are available, but these POS taggers can not be applied on the Hindi language as Hindi is one of the most morphologically rich language. Furthermore there is a significant difference between the morphological structures of these languages. Thus in this work, a POS tagger system is presented for the Hindi language. For Hindi POS tagging a hybrid approach is presented in this paper which combines "Probability-based and Rule-based" approaches. For known word tagging a Unigram model of probability class is used, whereas for tagging unknown words various lexical and contextual features are used. Various finite state machine automata are constructed for demonstrating different rules and then regular expressions are used to implement these rules. A tagset is also prepared for this task, which contains 29 standard part-of-speech tags. The tagset also includes two unique tags, i.e., date tag and time tag. These date and time tags support all possible formats. Regular expressions are used to implement all pattern based tags like time, date, number and special symbols. The aim of the presented approach is to increase the correctness of an automatic Hindi POS tagging while bounding the requirement of a large human-made corpus. This hybrid approach uses a probability-based model to increase automatic tagging and a rule-based model to bound the requirement of an already trained corpus. This approach is based on very small labeled training set (around 9,000 words) and yields 96.54% of best precision and 95.08% of average precision. The approach also yields best accuracy of 91.39% and an average accuracy of 88.15%.