• Title/Summary/Keyword: language processing

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BIOLOGY ORIENTED TARGET SPECIFIC LITERATURE MINING FOR GPCR PATHWAY EXTRACTION (GPCR 경로 추출을 위한 생물학 기반의 목적지향 텍스트 마이닝 시스템)

  • KIm, Eun-Ju;Jung, Seol-Kyoung;Yi, Eun-Ji;Lee, Gary-Geunbae;Park, Soo-Jun
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2003.10a
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    • pp.86-94
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    • 2003
  • Electronically available biological literature has been accumulated exponentially in the course of time. So, researches on automatically acquiring knowledge from these tremendous data by text mining technology become more and more prosperous. However, most of the previous researches are technology oriented and are not well focused in practical extraction target, hence result in low performance and inconvenience for the bio-researchers to actually use. In this paper, we propose a more biology oriented target domain specific text mining system, that is, POSTECH bio-text mining system (POSBIOTM), for signal transduction pathway extraction, especially for G protein-coupled receptor (GPCR) pathway. To reflect more domain knowledge, we specify the concrete target for pathway extraction and define the minimal pathway domain ontology. Under this conceptual model, POSBIOTM extracts interactions and entities of pathways from the full biological articles using a machine learning oriented extraction method and visualizes the pathways using JDesigner module provided in the system biology workbench (SBW) [14]

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Donguibogam-Based Pattern Diagnosis Using Natural Language Processing and Machine Learning (자연어 처리 및 기계학습을 통한 동의보감 기반 한의변증진단 기술 개발)

  • Lee, Seung Hyeon;Jang, Dong Pyo;Sung, Kang Kyung
    • The Journal of Korean Medicine
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    • v.41 no.3
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    • pp.1-8
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    • 2020
  • Objectives: This paper aims to investigate the Donguibogam-based pattern diagnosis by applying natural language processing and machine learning. Methods: A database has been constructed by gathering symptoms and pattern diagnosis from Donguibogam. The symptom sentences were tokenized with nouns, verbs, and adjectives with natural language processing tool. To apply symptom sentences into machine learning, Word2Vec model has been established for converting words into numeric vectors. Using the pair of symptom's vector and pattern diagnosis, a pattern prediction model has been trained through Logistic Regression. Results: The Word2Vec model's maximum performance was obtained by optimizing Word2Vec's primary parameters -the number of iterations, the vector's dimensions, and window size. The obtained pattern diagnosis regression model showed 75% (chance level 16.7%) accuracy for the prediction of Six-Qi pattern diagnosis. Conclusions: In this study, we developed pattern diagnosis prediction model based on the symptom and pattern diagnosis from Donguibogam. The prediction accuracy could be increased by the collection of data through future expansions of oriental medicine classics.

A New Morphological Analysis for the Spoken Language Translation System (음성언어 번역 시스템을 위한 새로운 형태소 분석)

  • 양승원;김재훈
    • The Journal of the Acoustical Society of Korea
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    • v.18 no.4
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    • pp.17-22
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    • 1999
  • It is difficult to integrate the speech processing systems and machine translation system in the spoken language translation system by reason that each system uses its own data and basic processing unit. So, we need a common I/O unit which is used in the whole system. In this paper, we propose a Pscudo-Morpheme as the interface between speech processing systems and language translation system. We implement a morphological analysis system for Pseudo-morpheme. The speech processing system using this pseudo-morpheme can get better result than other systems using the phrase or the general morpheme. So, the quality of the whole spoken language translation system can be improved. The analysis-ratio of our implemented system is 98.9%. This is similar to the common morphological analysis systems.

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On the Role of Prefabricated Speech in L2 Acquisition Process: An Information Processing Approach

  • Boo, Kyung-Soon
    • Annual Conference on Human and Language Technology
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    • 1991.10a
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    • pp.196-208
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    • 1991
  • This study focused on the role of prefabricated speech (routines and patterns) in the L2 acquisition process. The data for this study consisted of spontaneous speech samples and various observational records of three Korean children learning English as L2 in a nursery school. The specific questions addressed here were: (1) What routines, patterns, and creative constructions did the children use? (2) What was the general trend in the three children's use of routines, patterns, and creative constructions over time? The data were collected over a period of one school year by observing the children in their school. The findings were discussed from the perspective of human information processing. This study found that prefabricated speech played a significant role in the three children's L2 acquisition. The automatic processing of prefabricated speech appeared to enable the children to reduce the burden on their information processing systems, which allowed the saved resources available for other language development activities. Also, the children's language development was evident in their increase in the use of patterns. The children were moving from heavy dependence on wholly unanalyzed routines to increased use of partly unanalyzed patterns. This increased control was the result of an increase in procedural knowledge.

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Token-Based Classification and Dataset Construction for Detecting Modified Profanity (변형된 비속어 탐지를 위한 토큰 기반의 분류 및 데이터셋)

  • Sungmin Ko;Youhyun Shin
    • The Transactions of the Korea Information Processing Society
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    • v.13 no.4
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    • pp.181-188
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    • 2024
  • Traditional profanity detection methods have limitations in identifying intentionally altered profanities. This paper introduces a new method based on Named Entity Recognition, a subfield of Natural Language Processing. We developed a profanity detection technique using sequence labeling, for which we constructed a dataset by labeling some profanities in Korean malicious comments and conducted experiments. Additionally, to enhance the model's performance, we augmented the dataset by labeling parts of a Korean hate speech dataset using one of the large language models, ChatGPT, and conducted training. During this process, we confirmed that filtering the dataset created by the large language model by humans alone could improve performance. This suggests that human oversight is still necessary in the dataset augmentation process.

Spatial Big Data Query Processing System Supporting SQL-based Query Language in Hadoop (Hadoop에서 SQL 기반 질의언어를 지원하는 공간 빅데이터 질의처리 시스템)

  • Joo, In-Hak
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.10 no.1
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    • pp.1-8
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    • 2017
  • In this paper we present a spatial big data query processing system that can store spatial data in Hadoop and query the data with SQL-based query language. The system stores large-scale spatial data in HDFS-based storage system, and supports spatial queries expressed in SQL-based query language extended for spatial data processing. It supports standard spatial data types and functions defined in OGC simple feature model in the query language. This paper presents the development of core functions of the system including query language parsing, query validation, query planning, and connection with storage system. We compares the performance of the suggested system with an existing system, and our experiments show that the system shows about 58% performance improvement of query execution time over the existing system when executing region query for spatial data stored in Hadoop.

Robust Part-of-Speech Tagger using Statistical and Rule-based Approach (통계와 규칙을 이용한 강인한 품사 태거)

  • Shim, Jun-Hyuk;Kim, Jun-Seok;Cha, Jong-Won;Lee, Geun-Bae
    • Annual Conference on Human and Language Technology
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    • 1999.10d
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    • pp.60-75
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    • 1999
  • 품사 태깅은 자연 언어 처리의 가장 기본이 되는 부분으로 상위 자연 언어 처리 부분인 구문 분석, 의미 분석의 전처리로 사용되고, 독립된 응용으로 언어의 정보를 추출하거나 정보 검색 등의 응용에 사용되어 진다. 품사 태깅은 크게 통계에 기반한 방법, 규칙에 기반한 방법, 이 둘을 모두 이용하는 혼합형 방법 등으로 나누어 연구되고 있다. 포항공대 자연언어처리 연구실의 자연 언어 처리 엔진(SKOPE)의 품사 태깅 시스템 POSTAG는 미등록어 추정이 강화된 혼합형 품사 태깅 시스템이다 본 시스템은 형태소 분석기, 통계적 품사 태거, 에러 수정 규칙 후처리기로 구성되어 있다. 이들은 각각 단순히 직렬 연결되어 있는 것이 아니라 형태소 접속 테이블을 기준으로 분석 과정에서 형태소 접속 그래프를 생성하고 처리하면서 상호 밀접한 연관을 가진다. 그리고, 미등록어용 패턴사전에 의해 등록어와 동일한 방법으로 미등록어를 처리함으로써 효율적이고 강건한 품사 태깅을 한다. 한편, POSTAG에서 사용되는 태그세트와 한국전자통신연구원(ETRI)의 표준 태그세트 간에 양방향으로 태그세트 매핑을 함으로써, 표준 태그세트로 태깅된 코퍼스로부터 POSTAC를 위한 대용량 학습자료를 얻고 POSTAG에서 두 가지 태그세트로 품사 태깅 결과 출력이 가능하다. 본 시스템은 MATEC '99'에서 제공된 30000어절에 대하여 표준 태그세트로 출력한 결과 95%의 형태소단위 정확률을 보였으며, 태그세트 매핑을 제외한 POSTAG의 품사 태깅 결과 97%의 정확률을 보였다.

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Construction of Immunology Thesaurus and Ontology (면역학 시소러스 및 온톨로지 구축)

  • Im, Ji-Hui;Choe, Ho-Seop;Bae, Young-Jun;Ock, Cheol-Young;Choi, Sung-Pil;Sung, Won-Kyung;Park, Dong-In
    • Annual Conference on Human and Language Technology
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    • 2005.10a
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    • pp.21-27
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    • 2005
  • 본 논문에서는 국가에서 추진하는 차세대신성장동력산업과 관련된 특정 분야('바이오 신약/장기' 분야 중 '면역 기능 제어')를 선택하여, 기구축된 면역학 전문용어사전을 비롯하여 의학용어사전, 표준국어대사전 등을 참조하여 핵심 용어와 관련 용어를 중심으로 면역학 시소러스(어휘 3,462개) 및 온톨로지(개념 노드 4,703개)를 구축하였다. 이것은 전문용어사전부터 온톨로지에 이르기까지 통일화된 표준 체계를 가지고 있으며, 도메인 온톨로지를 구축하여 향후 온톨로지 개발 방향을 설정할 수 있는 계기가 되었다고 할 수 있다. 또한 면역학 시소러스는 검색의 성능을 향상시킬 수 있도록 충분한 양의 데이터를 구축하였고 면역학 온톨로지는 언어처리적 관점에서의 온톨로지를 표현하였다. 이는 정보검색에서의 효율성을 비롯하여, 특정 웹 온톨로지 언어를 이용한 웹 온톨로지로의 변환성, 대규모 도메인 온톨로지라는 점에서 의미를 가진다고 할 수 있다.

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