• Title/Summary/Keyword: semantics fields

Search Result 15, Processing Time 0.023 seconds

Design of Alert Message and Message Segmentation For T-DMB Automatic Emergency Alert Service Standard (지상파 DMB 자동 재난경보방송 표준을 위한 재난 메시지 및 메시지 분할 방법 설계)

  • Choi, Seong-Jong
    • Journal of Broadcast Engineering
    • /
    • v.15 no.2
    • /
    • pp.304-312
    • /
    • 2010
  • This paper presents the design of alert message and message segmentation method for the Terrestrial DMB Automatic Emergency Alert Service Standard. This paper begins with the introduction of the standard and some background. The alert message contains the essential information of the alert. Structured information is represented as coded fields and the unstructured as short sentences. Design factors of each field are analyzed first. Next each field’s semantics and syntax are described. The message segmentation method is presented next. The previous study selected FIDC as the message delivery channel. However, in most cases, the size of the alert message exceeds the maximum size of FIG, which is the basic unit packet for the channel. The current T-DMB standard does not support the segmentation method for FIDC. To solve the problem, this paper proposes an elegant segmentation method that garantees reliable, flexible, and fast receiver characteristic. This paper concludes with some additional tasks that should be resolved before the full-time alert service.

An Object Oriented Data Model of a Spatiotemporal Geographic-Object Based on Attribute Versioning (속성 버전화에 기반한 시공간 지리-객체의 객체 지향 데이터 모델)

  • Lee, Hong-Ro
    • Journal of the Institute of Electronics Engineers of Korea CI
    • /
    • v.38 no.6
    • /
    • pp.1-17
    • /
    • 2001
  • Nowadays, spatiotemporal data models deal with objects which can be potentially useful for wide range applications in order to describe complex objects with spatial and/or temporal facilities. However, the information needed by each application usually varies, specially in the geographic information which depends on the kind of time oriented views, as defined in the modeling phase of the spatiotemporal geographic data design. To be able to deal with such diverse needs, geographic information systems must offer features that manipulate geometric, space-dependent(i.e, thematic), and spatial relationship positions with multiple time oriented views. This paper addresses problems of the formal definition of relationships among spatiotemporal objects and their properties on geographic information systems. The geographical data are divided in two main classes : geo-objects and geo-fields, which describe discrete and continuous representations of the spatial reality. I study semantics and syntax about the temporal changes of attributes and the relationship roles on geo-objects and non-geo-objects, This result will contribute on the design of object oriented spatiotemporal data model which is distinguishied from the recent geographic information system of the homogeneously anchored spatial objects

  • PDF

A study on research trends for gestational diabetes mellitus and breastfeeding: Focusing on text network analysis and topic modeling (임신성 당뇨와 모유수유에 대한 연구 동향 분석: 텍스트네트워크 분석과 토픽모델링 중심)

  • Lee, Junglim;Kim, Youngji;Kwak, Eunju;Park, Seungmi
    • The Journal of Korean Academic Society of Nursing Education
    • /
    • v.27 no.2
    • /
    • pp.175-185
    • /
    • 2021
  • Purpose: The aim of this study was to identify core keywords and topic groups in the 'Gestational diabetes mellitus (GDM) and Breastfeeding' field of research for better understanding research trends in the past 20 years. Methods: This was a text-mining and topic modeling study composed of four steps: 1) collecting abstracts, 2) extracting and cleaning semantic morphemes, 3) building a co-occurrence matrix, and 4) analyzing network features and clustering topic groups. Results: A total of 635 papers published between 2001 and 2020 were found in databases (Web of Science, CINAHL, RISS, DBPIA, RISS, KISS). Among them, 3,639 words extracted from 366 articles selected according to the conditions were analyzed by text network analysis and topic modeling. The most important keywords were 'exposure', 'fetus', 'hypoglycemia', 'prevention' and 'program'. Six topic groups were identified through topic modeling. The main topics of the study were 'cardiovascular disease' and 'obesity'. Through the topic modeling analysis, six themes were derived: 'cardiovascular disease', 'obesity', 'complication prevention strategy', 'support of breastfeeding', 'educational program' and 'management of GDM'. Conclusion: This study showed that over the past 20 years many studies have been conducted on complications such as cardiovascular diseases and obesity related to gestational diabetes and breastfeeding. In order to prevent complications of gestational diabetes and promote breastfeeding, various nursing interventions, including gestational diabetes management and educational programs for GDM pregnancies, should be developed in nursing fields.

A Study on the Contemporary Definition of 'GARDEN' - Keyword Analysis used Literature Research and Big Data - ('정원'의 시대적 정의에 관한 연구 - 문헌연구와 빅데이터를 활용한 키워드 분석을 중심으로-)

  • Woo, Kyungsook;Suh, Joo Hwan
    • Journal of the Korean Institute of Landscape Architecture
    • /
    • v.44 no.5
    • /
    • pp.1-11
    • /
    • 2016
  • There has been an increasingly high interest in gardens and garden design in Korea recently. However, the usage of the term 'garden' is extremely varied and complex, and there has been very little academic research made on the meaning of garden. Therefore, this research attempts to investigate the ideas of current gardens and to elucidate their changing patterns by means of extensive literature research and big data analysis. The notion of garden in the past was broad including not only private space such as Madang(마당) and Teul(뜰), but also even field and grass land as public outdoor space. Yet, the meaning has become smaller to merely private space due to the change of dwelling systems due to high industrial development of the 20th century. Furthermore, the introduction of urban parks as an interactive space between nature and humans, the similar spatial function of gardens, has blurred the boundary between garden and park, which created confusion in understanding the concept of a garden. After all, garden is a subject for humans. The meanings of garden need to be recognized from various points of view since garden itself is a creation by the sum of diverse fields such as natural and social sciences as well as culturology. This discussion on the meaning of garden in the present day will give a conceptual foundation for future research on gardens and garden design. Also, the big data analysis employed here as a research method can help other similar research topics, particularly semantics in landscape architecture.

KNU Korean Sentiment Lexicon: Bi-LSTM-based Method for Building a Korean Sentiment Lexicon (Bi-LSTM 기반의 한국어 감성사전 구축 방안)

  • Park, Sang-Min;Na, Chul-Won;Choi, Min-Seong;Lee, Da-Hee;On, Byung-Won
    • Journal of Intelligence and Information Systems
    • /
    • v.24 no.4
    • /
    • pp.219-240
    • /
    • 2018
  • Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.