Research DEMO

Stemming from corpora, speech, Eojeol, usage, basil, word search, and support neighborhood

DEMO : Corpus Information Retrieval Link

Document classification using KNN algorithm.

DEMO : Document Classifier Link

Serious games to improve cognitive skills.

DEMO : Cognitive Enhancement Games Link

GoStop game using brain waves.

Korean language IME brain computer using predictive models.

Korean morphological analyzer using a probability model

DEMO : Korean morphological analyzer Link

Korean auto spacing using probabilistic models Braces

DEMO :혻Korean auto spacer Link

COLLA is a platform to build social networks among students who share the same class, subject or interests

DEMO : Social Learning Platform COLLA Link

EDIAGNOSIS is a system based on Lexical Decision Task to get users English proficiency by analyzing response time and accuracy result consists of individual tests of Reading, Listening, and Translation.

DEMO :혻EDIAGNOSIS Link

Guest ID : test@test.com,혻Password : 123456

APHASIA is a Computer based rehabilitation training system for patients with aphasia

DEMO :혻APHASIA Link

MOOT뒗 븰뒿옄쓽 옄쑉꽦쓣 諛뷀깢쑝濡 쑀뿰븳 븰뒿솢룞씠 릺寃뚮걫 젣怨듯븿쑝濡쒖뜥 옄湲곗<룄쟻씤 븰뒿쓣 븷 닔 엳쑝硫, 씪諛 援먭낵꽌 떎瑜닿쾶 뿬윭 옄猷뚯쑀삎쓣 젣怨듯븳떎.

 

DEMO : Massive Open Online Textbook Link

 

 

This demo uses Komoran 3 for morphological analysis and POS tagging, which is used as an input to SyntaxNet which performs dependency parsing. Dependency parsing determines dependencies among words in a sentence, which can pave the way for sentiment analysis and could also be used to improve machine translation. The generated SyntaxNet file is then passed through a tree visualization tool and output in SVG format.

 

Five tabs are provided: POS Tagging, Dependency Parsing, Reassembly, Semantic Analysis, and Debug. POS Tagging and Dependency Parsing show the POS tags and parsing results of the original sentence components, and reassembly shows these components recombined back into Korean words (Eojeol). Semantic Analysis tab uses experimental Wikipedia taxonomy analysis and multilingual link database to show Korean and English translations for some terms. The Debug tab can be used to check more details if an error occurs.

 

For more details on how SyntaxNet works under the hood, check out Andrew’s documentation on his blog here:

 

The SyntaxNet seminar is also available on the Seminars page.

 

Dense real-valued vector representations of words or word embedding have recently gained increasing popularity in natural language processing, serving as invaluable features in a broad range of NLP tasks.

Research interest has recently extended to bilingual word embeddings. Bilingual word embedding models focus on the induction of a shared bilingual word embedding space where words from both languages are represented in a uniform language-independent manner such that similar words have similar representation.

These research goal make seed lexicon by aligned parallel data and make mapping function between seed lexicon and general-domain corpora such as Wikipedia. Therefore, we expect to applicate these research to other NLP tasks such as machine translation.

 

떎뻾諛⑸쾿 : 留곹겕 겢由 썑 쇊履 諛붿뿉꽌 road data 겢由 썑 preload example 겢由

Project혻DEMO

怨쇱젣 紐 : 媛쒖씤怨 吏묐떒吏꽦쓽 뵒吏꽭肄섑뀗痢좏솕瑜 넻븳 쑀넻 諛 솗궛 꽌鍮꾩뒪 湲곗닠 媛쒕컻

븯굹쓽 뵆옯뤌 븞뿉꽌 깮궛, 냼鍮, 쑀넻 嫄곕옒 떆뒪뀥쓽 꽌鍮꾩뒪瑜 쐞븳 HTML5湲곕컲쓽 諛섏쓳삎 쎒 궗씠듃 吏삙留덉폆쓣 媛쒕컻븯떎. 벑濡앸맂 뵒吏꽭 肄섑뀗痢좊뒗 紐⑤뱢삎 솗옣 援ъ“濡 愿由ы븳떎.

 

吏삙留덉폆

DEMO : Wisdom Market Link

怨쇱젣 紐 : 媛쒖씤怨 吏묐떒吏꽦쓽 뵒吏꽭肄섑뀗痢좏솕瑜 넻븳 쑀넻 諛 솗궛 꽌鍮꾩뒪 湲곗닠 媛쒕컻

蹂 뿰援щ뒗 궗슜옄媛 썝븯뒗 寃곌낵 썝移 븡뒗 寃깋 寃곌낵媛 꽎뿬 굹굹뒗 寃껋쓣 諛⑹븯怨 寃깋뿉 뱶뒗 떆媛꾧낵 끂젰쓣 理쒖냼솕븯뿬 궗슜옄뿉寃 렪由ъ꽦怨 留뚯”룄瑜 뼢긽떆궗 닔 엳뒗 “뵒吏꽭 肄섑뀗痢 媛쒖씤솕 寃깋”씠떎. 蹂 뿰援щ 넻븯뿬 寃깋 슚쑉쓣 뼢긽떆궎怨 냼鍮꾩옄쓽 寃깋 寃곌낵뿉 븳 留뚯”룄瑜 利앸떆耳쒖꽌 뵒吏꽭 肄섑뀗痢 냼鍮꾨 솢꽦솕 떆궎怨좎옄 븳떎.

 

洹몃┝1

DEMO : Contents Recommendation System Link

怨쇱젣 紐 : 媛쒖씤怨 吏묐떒吏꽦쓽 뵒吏꽭肄섑뀗痢좏솕瑜 넻븳 쑀넻 諛 솗궛 꽌鍮꾩뒪 湲곗닠 媛쒕컻

蹂 뿰援щ뒗 뵒吏꽭 肄섑뀗痢 寃깋 湲곗닠쓣 媛쒖씤솕 寃깋湲곗닠濡 怨좊룄솕 떆耳 궗슜옄 꽦뼢 諛 愿떖궗瑜 怨좊젮븯뿬 궗슜옄媛 썝븯뒗 寃깋 寃곌낵瑜 젣怨듯븯뒗 寃껋쓣 紐⑹쟻쑝濡 븳떎.

 

삙꽦삤鍮

DEMO : Personalized Information Retrieval System Link

怨쇱젣 紐 : 媛쒖씤怨 吏묐떒吏꽦쓽 뵒吏꽭肄섑뀗痢좏솕瑜 넻븳 쑀넻 諛 솗궛 꽌鍮꾩뒪 湲곗닠 媛쒕컻

蹂 뿰援щ뒗 웾쓽 肄섑뀗痢 븞뿉꽌 궗슜옄媛 슚쑉쟻씤 븰뒿쓣 븷 닔 엳룄濡 븰뒿 肄붿뒪瑜 옄룞쑝濡 깮꽦빐以뚯쑝濡쒖뜥 궗슜옄뱾씠 냼鍮꾪븳 肄섑뀗痢 씠젰쓣 湲곕컲쑝濡 떎瑜 궗슜옄뱾씠 븿猿 냼鍮꾪븳 肄섑뀗痢좊 넻븯뿬 븰뒿빐빞븷 몴쟻씤 븰뒿肄붿뒪瑜 옄룞쑝濡 깮꽦빐二쇰뒗 寃껋쓣 紐⑹쟻쑝濡 븳떎.

 

2

DEMO : Learning Course Recommendation System Link

怨쇱젣 紐 : 媛쒖씤怨 吏묐떒吏꽦쓽 뵒吏꽭肄섑뀗痢좏솕瑜 넻븳 쑀넻 諛 솗궛 꽌鍮꾩뒪 湲곗닠 媛쒕컻

삩씪씤 援먯쑁 ICT 븿猿 吏냽쟻씤 諛쒖쟾쓣븯怨 엳떎. 洹몄뿉 뵲씪꽌 삩씪씤 븰뒿옄뒗 利앷븯怨 엳쑝硫 援먯닔옄뒗 븰뒿옄뱾쓽 븰뒿 뿬遺瑜 븣湲 썝븳떎. 蹂 뿰援щ뒗 鍮꾨뵒삤 肄섑뀗痢 湲곕컲쓽 삩씪씤 援먯쑁뿉꽌 븰뒿옄뱾쓽 理쒖냼 븰뒿 뿬遺瑜 뙋떒븷 닔 엳뒗 理쒖냼븰뒿 紐⑤뜽쓣 젣븞븯떎. 洹몃━怨 理쒖냼븰뒿 뙋떒 떆뒪뀥쓣 꽕怨 諛 媛쒕컻븯떎.

蹂 뿰援щ뒗 븰뒿옄뱾씠 삩씪씤 援먯쑁솚寃쎌뿉꽌 鍮꾨뵒삤 肄섑뀗痢좊 넻빐 理쒖냼븳쓽 븰뒿씠 씠猷⑥뼱吏 긽깭瑜 理쒖냼 븰뒿씠씪怨 젙쓽븳떎. 理쒖냼븰뒿 븰뒿옄뱾씠 鍮꾨뵒삤 肄섑뀗痢좊 떆泥뻽쓣 븣 鍮꾨뵒삤 肄섑뀗痢좎뿉꽌 궗슜맂 떒뼱쓽 鍮덈룄슚怨쇰 湲곕컲쑝濡 씤吏泥섎━怨쇱젙쓣넻빐 理쒖냼븳쓽 鍮꾨뵒삤 肄섑뀗痢 떆泥쓣 뻽뒗吏뿉븳 뿬遺瑜 뙋떒븯怨 鍮꾨뵒삤 肄섑뀗痢좊 吏묒쨷븯뿬 떆泥 뻽떎뒗 寃껋 理쒖냼븳쓽 븰뒿 뻾룞씠 씠猷⑥뼱 議뚮떎뒗 寃껋쓣 쓽誘명븳떎.

 

unnamed

<洹몃┝. 理쒖냼븰뒿 뙋떒쓣 쐞븳 떒뼱寃뚯엫>

DEMO : Minimum Learning Judgement System Link

怨쇱젣 紐 : 媛쒖씤怨 吏묐떒吏꽦쓽 뵒吏꽭肄섑뀗痢좏솕瑜 넻븳 쑀넻 諛 솗궛 꽌鍮꾩뒪 湲곗닠 媛쒕컻

뵒吏꽭 肄섑뀗痢좎쓽 醫낅쪟 닔媛 湲고븯湲됱닔쟻쑝濡 利앷븿뿉 뵲씪 궗슜옄뱾 肄섑뀗痢 꽑깮뿉 뼱젮쓣 뒓겮怨, 떆媛꾩쓣 냼鍮꾪븯寃 맂떎. 씠뿉 뵒吏꽭 肄섑뀗痢 꽑깮뿉 떆媛꾩냼鍮꾩 뼱젮쓣 빐냼븷 닔 엳뒗 뵒吏꽭 肄섑뀗痢 룊뙋룄 痢≪젙 븣怨좊━利섏쓣 젣븞븯떎.
蹂 뿰援щ뒗 뵒吏꽭 肄섑뀗痢좎쓽 吏덉쟻 룊媛 궗슜옄 肄섑뀗痢, 궗슜옄 궗슜옄媛꾩쓽 긽샇옉슜쓣 遺꾩꽍 諛 痢≪젙븯뿬 肄섑뀗痢좊 닚쐞솕븯뿬 긽쐞 肄섑뀗痢좊 궗슜옄뿉寃 젣怨듯븳떎.

repu

DEMO : Digital Contents Reputation System

怨쇱젣 紐 : 媛쒖씤怨 吏묐떒吏꽦쓽 뵒吏꽭肄섑뀗痢좏솕瑜 넻븳 쑀넻 諛 솗궛 꽌鍮꾩뒪 湲곗닠 媛쒕컻

삩씪씤긽뿉꽌 뵒吏꽭 肄섑뀗痢좉 젏李 利앷븿뿉 뵲씪 궗슜옄뱾씠 肄섑뀗痢 꽑깮뿉 뼱젮쓣 寃り퀬 엳떎. 씠뿉 뵲씪 삩씪씤 꽌鍮꾩뒪 궗슜옄뱾 肄섑뀗痢 꽑깮뿉 룄쓣 諛쏄린瑜 썝븳떎. 씠뿉 뵲씪 궗슜옄쓽 쓽궗寃곗젙 臾몄젣 떎瑜 븰뒿옄뱾쓽 뻾룞 蹂솕 媛먯뿉 븳 뼱젮쓣 빐寃고븯湲 쐞빐 떆媛곹솕 湲곕쾿쓣 궗슜븯뿬 蹂 떆뒪뀥쓣 媛쒕컻븯떎.

蹂 뿰援щ뒗 궗슜옄濡쒕꽣 異붿텧븳 뵒吏꽭 뜲씠꽣瑜 삩씪씤 꽌鍮꾩뒪 궗슜옄뱾씠 씠빐븯湲 돩슫 삎깭濡 紐⑤뜽留곹븯쑝硫, 뵒吏꽭 肄섑뀗痢좎 궗슜옄 媛꾩쓽 뿰愿꽦쓣 씠빐븯湲 돺룄濡 뜲씠꽣 떆媛곹솕 븯떎. 蹂 떆뒪뀥쓣 넻빐 뼱뼡 궗슜옄媛 뼱뼡 肄섑뀗痢좊 떆泥븯뒗吏, 洹 肄섑뀗痢 븞뿉꽌뒗 뼹留덈굹 留롮 솢룞씠 씪뼱굹怨 엳뒗吏 벑 뵒吏꽭 肄섑뀗痢좎 궗슜옄 媛꾩쓽 愿怨꾨 븳늿뿉 궡렣蹂닿퀬 씠빐븷 닔 엳떎.

젣紐 뾾쓬

<洹몃┝. 뜲씠꽣 떆媛곹솕 떆뒪뀥 삁떆>

DEMO : Personalized Interest Map Link

怨쇱젣 紐 : 媛쒖씤怨 吏묐떒吏꽦쓽 뵒吏꽭肄섑뀗痢좏솕瑜 넻븳 쑀넻 諛 솗궛 꽌鍮꾩뒪 湲곗닠 媛쒕컻

蹂 뿰援щ뒗 떎떆媛꾩쑝濡 븰뒿옄쓽 븰뒿 솢룞쓣 紐⑤땲꽣留곹븷 닔 엳룄濡 젣怨듯븯怨 뜲씠꽣瑜 돺寃 씠빐븷 닔 엳寃 RDF(Resource Description Framework) 뜲씠꽣 紐⑤뜽濡 몴쁽븷 닔 엳룄濡 媛쒕컻븯뒗 寃껋쓣 紐⑹쟻쑝濡 븳떎.

 

datashop

DEMO : BLP Data Shop Link