• Korean Journal of Cognitive Science
• /
• v.23 no.3
• /
• pp.295-322
• /
• 2012

In this paper, we cluster similar word senses applying vector space model and HAL (Hyperspace Analog to Language). HAL measures corelation among words through a certain size of context (Lund and Burgess 1996). The similarity measurement between a word pair is cosine similarity based on the vector space model, which reduces distortion of space between high frequency words and low frequency words (Salton et al. 1975, Widdows 2004). We use PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) to reduce a large amount of dimensions caused by similarity matrix. For sense similarity clustering, we adopt supervised and non-supervised learning methods. For non-supervised method, we use clustering. For supervised method, we use SVM (Support Vector Machine), Naive Bayes Classifier, and Maximum Entropy Method.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.7
• /
• pp.2999-3001
• /
• 2015

Background: This study aimed to evaluate upper gastrointestinal polyps detected during esophago-gastroduodenoscopy tests. Materials and Methods: We conducted a retrospective analysis on data regarding 55,987 upper gastrointestinal endoscopy tests performed at the endoscopy unit of Istanbul Education and Research Hospital between January 2006 and June 2012. Results: A total of 66 upper gastrointestinal polyps from 59 patients were analyzed. The most common clinical symptom was dyspepsia, observed in 41 cases (69.5%). The localizations of the polyps were as follows: 29 in the antrum (43.9%), 15 in the corpus (22.7%), 11 in the cardia (16.7%), 3 in the fundus (4.54%), 3 in the second portion of the duodenum (4.54%), 2 in the bulbus (3.03%) and 3 in the lower end of the esophagus (4.54%). Histopathological types of polyps included hyperplastic polyps (44) (66.7%), faveolar hyperplasia (8) (12.1%), fundic gland polyps (4) (6.06%), squamous cell polyps (4) (6.06%), hamartomatous polyps (3) (4.54%), and pyloric gland adenoma (3) (4.54%). Histopathological analysis of the gastric mucosa showed chronic atrophic gastritis in 30 cases (50.84%), HP infection in 33 cases (55.9%) and intestinal metaplasia in 19 cases (32.20%). In 3 cases with multiple polyps, adenocarcinoma was detected in hyperplastic polyps. Conclusions: Among polypoid lesions of the upper gastrointestinal tract, the most common histological type is hyperplastic polyps. Generally, HP infection is associated with chronic atrophic gastritis and intestinal metaplasia. The incidence of adenocarcinoma tends to be higher in patients with multiple hyperplastic polyps.

• The Journal of the Korea Contents Association
• /
• v.12 no.12
• /
• pp.35-45
• /
• 2012

Researchers and scientists spend huge amount of time in analyzing the previous studies and their results. In order to timely take the advantageous position, they usually analyze various resources such as paper, patents, and Web documents on recent research issues to preoccupy newly emerging technologies. However, it is difficult to select invest-worthy research fields out of huge corpus by using the traditional information search based on keywords and bibliographic information. In this paper, we propose a method for efficient creation, storage, and utilization of semantically relevant information among technologies, products and research agents extracted from 'big data' by using text mining. In order to implement the proposed method, we designed an ontology that creates technological knowledge for semantic web environment based on the relationships extracted by text mining techniques. The ontology was utilized for InSciTe Adaptive, a R&D trends analysis and forecast service which supports the search for the relevant technological knowledge.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.8
• /
• pp.3131-3135
• /
• 2015

Objective: To investigate whether the expression of serum soluble neural cell adhesion molecule (sNCAM) is associated with hepatic encephalopathy (HE) in hepatocelular carcinoma (HCC) patients. Materials and Methods: The Oncomine Cancer Microarray database was used to determine the clinical relevance of NCAM expression in different kinds of human cancers. Sera from 75 HCC cases enrolled in this study were assessed for expression of sNCAM by enzyme linked immunosorbent assay (ELISA). Results: Dependent on the Oncomine Cancer Microarray database analysis, NCAM was down regulated in 10 different kinds of cancer, like bladder cancer, brain and central nervous system cancer, while up-regulated in lung cancer, uterine corpus leiomyoma and sarcoma, compared to normal groups. Puzzlingly, NCAM expression demonstrated no significant difference between normal and HCC groups. However, we found by quantitative ELISA that the level of sNCAM in sera from HCC patients with HE ($347.4{\pm}151.9ng/ml$) was significantly more up-regulated than that in HCC patients without HE ($260.3{\pm}104.2ng/ml$), the p-value being 0.008. sNCAM may be an important risk factor of HE in HCC patients, the correlation coefficients was 0.278 (P<0.05) on rank correlation analysis. Conclusions: This study highlights that up-regulated level of serum sNCAM is associated with HE in HCC patients and suggests that the high expression can be used as an indicator.

• Journal of KIISE:Software and Applications
• /
• v.37 no.12
• /
• pp.919-927
• /
• 2010

Since the performance of mobile devices is recently improved, the requirement of information retrieval is increased in the mobile devices as well as PCs. If a mobile device with small memory uses a tradition language analysis tool to extract nouns from korean texts, it will impose a burden of analysing language. As a result, the need for the language analysis tools adequate to the mobile devices is increasing. Therefore, this paper proposes a new method for noun extraction using post-noun morpheme sequences and noun patterns from a large corpus. The proposed noun extractor has only the dictionary capacity of 146KB and its performance shows 0.86 $F_1$-measure; the capacity of noun dictionary corresponds to only the 4% capacity of the existing noun extractor with a POS tagger. In addition, it easily extract nouns for unknown word because its dependence for noun dictionaries is low.

• Journal of KIISE
• /
• v.42 no.4
• /
• pp.512-521
• /
• 2015

Internet users purchase goods on the Internet and express their positive or negative emotions of the goods in product reviews. Analysis of the product reviews become critical data to both potential consumers and to the decision making of enterprises. Therefore, the importance of opinion mining techniques which derive opinions by analyzing meaningful data from large numbers of Internet reviews. Existing studies were mostly based on comments written in English, yet analysis in Korean has not actively been done. Unlike English, Korean has characteristics of complex adjectives and suffixes. Existing studies did not consider the characteristics of the Internet language. This study proposes an emotional classification method which increases the accuracy of emotional classification by analyzing the characteristics of the Internet language connoting feelings. We can classify positive and negative comments about products automatically using the Internet emoticon. Also we can check the validity of the proposed algorithm through the result of high precision, recall and coverage for the evaluation of this method.

• Journal of Digital Contents Society
• /
• v.18 no.3
• /
• pp.585-592
• /
• 2017

As development of personal devices have made everyday use of internet much easier than before, it is getting generalized to find information and share it through the social media. In particular, communities specialized in each field have become so powerful that they can significantly influence our society. Finally, businesses and governments pay attentions to reflecting their opinions in their strategies. The stock market fluctuates with various factors of society. In order to consider social trends, many studies have tried making use of bigdata analysis on stock market researches as well as traditional approaches using buzz amount. In the example at the top, the studies using text data such as newspaper articles are being published. In this paper, we analyzed the post of 'Paxnet', a securities specialists' site, to supplement the limitation of the news. Based on this, we help researchers analyze the sentiment of investors by generating a domain-specific sentiment lexicon for the stock market.

• Journal of Information Processing Systems
• /
• v.18 no.4
• /
• pp.549-561
• /
• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.467-474
• /
• 2013

Construction contactors must continuously seek to improve their cash flows, which reside at the heart of their financial success. They require careful planning, analysis, and optimization to avoid the risk of bankruptcy, remain profitable, and secure long-term growth. Sources of cash include bank loans and retained earnings, which are conceptually similar in that they both incur a cost of capital. Financial management therefore requires accurate yet customizable modeling capabilities that can quantify all expenses, including said cost of capital. However, currently existing cash flow models in construction engineering and management have strongly simplified the manner in which interest is assessed, which may even lead to overstating it at a disadvantage to contractors. The variable nature of cash balances, especially in the early phases of construction projects, contribute to this challenging issue. This research therefore extends a new cash flow model with an accurate interest calculation. It utilizes singularity functions, so called because of their ability to flexibly model changes across any number of different ranges. The interest function is continuous for activity costs of any duration and allows the realistic case that activities may begin between integer time periods, which are often calendar months. Such fractional interest calculation has hitherto been lacking from the literature. It also provides insights into the self-referential behavior of compound interest for variable cash balances. The contribution of this study is twofold; augmenting the corpus of financial analysis theory with a new interest formula, whose strengths include its generic nature and that it can be evaluated at any fractional value of time, and providing construction managers with a tool to help improve and fine-tune the financial performance of their projects.

• Smart Media Journal
• /
• v.12 no.9
• /
• pp.19-27
• /
• 2023

Since morphemes are the smallest unit of meaning in Korean, it is necessary to develop an accurate morphemes analyzer to improve the performance of the Korean language model. However, most existing analyzers present morpheme analysis results by learning word unit tokens as input values. However, since Korean words are consist of postpositions and affixes that are attached to the root, even if they have the same root, the meaning tends to change due to the postpositions or affixes. Therefore, learning morphemes using word unit tokens can lead to misclassification of postposition or affixes. In this paper, we use morpheme-level tokens to grasp the inherent meaning in Korean sentences and propose a morpheme analyzer based on a sequence generation method using Transformer. In addition, a user dictionary is constructed based on corpus data to solve the out - of-vocabulary problem. During the experiment, the morpheme and morpheme tags printed by each morpheme analyzer were compared with the correct answer data, and the experiment proved that the morpheme analyzer presented in this paper performed better than the existing morpheme analyzer.