KOMORAN

Komoran 3.0

English | korean

Komoran stands for KO REAN MOR PHOLOGICAR , which is an Korean morphological analyzer embodied in Java.

Main features

• Implemented with pure java
  • Since it was developed only as 100% Java, it can be used anywhere in the environment where Java is installed.
• External library independent
  • There is no problem of dependence with external library using only its own Library.
• Light weight
  • It can also be operated on about 50MB of memory by handling self -unit processing and Trie dictionary.
• Easy usage
  • After applying the Library, you can use only one line in the source code to use a morphological analyzer.
• Easy to manage
  • It is composed of a regular text file, so it is highly readable and can be edited immediately.
• New analysis results
  • Unlike other morphological analyzers, it can be analyzed in a form of spaces containing spaces.

Demo and example

• You can check the analysis results in advance on the Komoran site.
• Input sentence: Korea is a democratic republic.

installation

Please refer to the 'Install' document.

Fast use

Please refer to the document, 'Follow the Morner Analysis in 3 minutes'.

Examples of use

• Analysis example
• Model learning example
• Spark2 Analysis Example (Scala)

KOMORAN reference material

This is a reference provided by SHINEWARE, which developed Komoran.

• You can see Komoran introduction and demo on the SHINEWARE homepage.
• You can refer to the Komoran installation and use in Komoran official document.
• Please visit Komoran Slack and share how to use and tips.

Official wrapper data

This is the official wrapper data developed by SHINEWARE.

• You can use Komoran for Python in Pykomoran.

Other reference materials

This is a reference that users have made.

• Hyunjoong Kim has released the Python version of Komoran3py (/Lovit/Komoran3py).

quotation

 @misc{komoran,
author = {Junsoo Shin, Junghwan Park, Geunho Lee},
title = {komoran},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {url{https://github.com/shineware/KOMORAN}}

Reference paper (2019 ~ 2020)

Domestic thesis

[2020]

• Woo Yun -hee, and Kim Hyun -hee. "Petition for prediction of national petition topics and deep learning -based answers." Information processing journal. Software and Data Engineering 9.2 (2020): 45-52.
• Choi Byung -seo, Lee Ik -hoon, and Lee Sang -gu. "A sequence strong in new words and spacing errors-to-sequence-based Korean morphological analyzers." 47.1 (2020): 70-77.
• Lee Hyun -seop, and Kim Jin -deok. "Similar video recommendation system design using extracted morphology from big data clusters." Korean Information and Communication Society Thesis 24.2 (2020): 172-178.

[2019]

• Park Jong -in, and Kim Nam -kyu. "Multiple vector documents through the meaningful decomposition of composite documents." Intelligent Information Research 25.3 (2019): 19-41.
• Ahn Jung -won, Kang Ye -mi, and Kim Gun -dong. "Study on user preference according to the type of answer when talking with AI speakers." Korean Design Society Conference Conference (2019): 227-228.
• Woo Kyung -jin, and Soo -hyun. "Comparison of Hangul Mold Analysis according to sentence type." The Korean Information Science Society Conference Book (2019): 1388-1390.
• Kim Si -eun, and Kim Min -soo. "Counseling chatbot design to identify emotions." Procedings of Kiit Conference. 2019.
• Lee Min -jung, and Kim Yong -hyun. "VOC type automatic classification." The Korean Industrial Engineering Society Fall Conference (2019): 2756-2780.
• Kim Hyun -ji, et al. "Changes in social perception through news frame analysis of schizophrenia and schizophrenia." Korean Literature Information Society Journal 53.4 (2019): 285-307.
• Sae -mi Lee, and Hong Soon -gu. "Blockchain trend analysis using the topic modeling technique." The Korea Information and Communication Society Women's ICT Conference Book (2019): 44-47.
• Han Jung -hoon, Gu So -hyun, and Lee Han -joo. "Press analysis of gender equality of men and women's athletes." Journal of the Korean Society of Social Sports 78 (2019): 217-229.
• Lee Hyun -seop, et al. "Designed words and morphological extract systems for keyword analysis." Korean Information and Communication Society's Conference Bar 23.2 (2019): 538-539.
• Yoon Jae -yeon. Product review summary technique considering additional information. 2019. PHD themis. Seoul National University Graduate School.
• Shin Jin -seop, et al. "How much should I preserve the document? The Korean Information Science Society's Academic Presentation collection (2019): 850-852
• Heo Kwang -ho, Ko Young -joong, and Seo Jung -yeon. "Low-multiple-language machine translation to improve translation of resource language." The Korean Information Science Society Conference Book (2019): 649-651.
• Park Chan -min, et al. "Korean elmo embedding decision." The Korean Information Science Society Conference Book (2019): 608-610.
• Choi Min -sung, and OND Hospital. "Comparison of emotional analysis of the BI-LSTM model according to the qualities of morphology." Procedings of Kiit Conference. 2019.
• Lee In -ah, and Kim Hye -jin. "Analysis of research trends related to domestic and foreign place using text mining techniques." Korea Biblia Society 30.2 (2019): 189-209.
• Hong Ki -hye, and Choi Min -ji. "Comparative analysis of school social welfare and educational welfare research trends using text network analysis." School Social Welfare 46 (2019): 25-51.
• Kim, Soohyon, et al. "Text Mining for Economic Analysis." Available at SSRN 3405781 (2019).
• Baejin. "Humanities writing methods using college writing and big data." Literature 143 (2019): 395-421.
• Jung Ji -soo, et al. "Research on relevant document classification system through document similarity." Broadcasting Engineering Journal 24.1 (2019): 77-86.
• Ko Myung -hyun, et al. "Research on the name of individual name for predicting efficient dialogue information." Broadcasting Engineering Journal 24.1 (2019): 58-66.

Overseas papers

[2020]

• Jee, H., M. Tamariz, and R. Shillcock. "The Meaning-Sound Systematicity Also Found in the Korean Language."
• Park, Seungsoo, and Manhee Lee. "Artas: Automatic Research Trend Analysis System for Information Security." Procedings of the 35th Annual ACM Symposium on Applied Computing. 2020.
• Choi, Nong Suk, Hansung Kim, and Dongyoung Sohn. "Mapping Social Distress: A Computational Approach to Spatiotemporal Distribution of Anxiety." Social Science Computer Review (2020): 0894439320914505.
• Jin, Hoon, and Dong-Won Joo. "Method and STEPS for Diagnosing the Possibility of Corporate Bankruptcy Using Massive News." IEIE Transactions on Smart Processing & Computing 9.1 (2020): 13-21.

[2019]

• Heo, YoOnseok, Sangwoo Kang, and Donghyun Yoo. "Multimodal Neural Machine Translation with Weakly Labeled Images." IEEE Access 7 (2019): 54042-54053.
• NAM, Chung-Hyeon, and Kyung-Sik Jang. "KNE: An Automatic Dictionary Expansion Method Using Use-Cases for Morphological Analysis." Journal of Information and Communication Convergence Engineering 17.3 (2019): 191-197.
• Lee, Joohong, Dongyoung Sohn, and Yong Suk Choi. "A Tool for Spatio-Temporal Analysis of Social Anxiety with Twitter Data." Procedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019.
• IHM, Sun-Young, Ji-Hye Lee, and Young-Ho Park. "Skip-gram-kr: Korean word Embedding for semantic clustering." IEEE Access 7 (2019): 39948-39961.
• Kwon, Sunjae, Youngjoong Ko, and Jungyun Seo. "Effective Vector Representation for the Korean NAMED-ENTY Recognition." Pattern Recognition Letters 117 (2019): 52-57.
• Kim, jayong, and y. yi mun. "A hybrid modeling app for an automated lyrics-rating system for adolescents." EUROPEAN Conference on Information Retrieval. Springer, Cham, 2019.
• Edmiston, Daniel, and Taeuk Kim. "Intrinsic Evaluation of Grammatical Information Within Word Embeddings." (2019).
• XU, Guanghao, Youngjoong Ko, and Jungyun Seo. "Improving Neural Machine Translation by Filtering Synthetic Parallel Data." Entropy 21.12 (2019): 1213.
• Kim, TAE-HO, et al. "Emotional Voice Conversion Using Multitask Learning with text-to-speech." ARXIV Preprint Arxiv: 1911.06149 (2019).
• Yoo, Kang Min, taeuk kim, and sang-go Lee. "Don't Just Scratch The Surface: Enhancing Word Representations for Korean with Hanja." ARXIV Preprint Arxiv: 1908.09282 (2019).
• Lee, Sang Yup, and Min Ho Ryu. "Exploring Characterists of Online News Comments and Commenters with Machine Learning Approaches." Telematics and Informatics 43 (2019): 101249.
• JAE-yon, Lee, and Kim Hyunjoo. "The Text-Mining of Munhwa (Culture): The Case of a Popular Magazine in 1930s Korea." Acta Koreana 22.2 (2019).
• Kong, Hyesoo, and Wooju Kim. "Generating SUMMARY SENTENCES Using Adversarialy Regularized Autoencoders With Conditional Context." Expert Systems with Applications 130 (2019): 1-11.
• Jo, Wonkwang, and Myoungsoon You. "News Media's Framing of Health Policy and ITS Implications for Government Communication: a text mining analysis of news Insurance coverage in South Korea. " Health Policy 123.11 (2019): 1116-1124.

Reference document (2019 ~ 2020)

• X-project, seeking to develop science and technology in public participation
• Comparison of separate performance of sentences by Korean sport analyzer
• Text Mining (Text Mining Iran (01)
• [1 person] I made a diary app ..!
• Text mining company application case
• KOMORAN Korean morphological analyzer application
• Let's do Sentiment Analysis in the Spark environment (1)