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Current Result Document :
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ÀÌÀü°Ç
ÇѱÛÁ¦¸ñ(Korean Title)
n-gram ÆÄƼŬÀ» ÀÌ¿ëÇÑ º£ÀÌÁö¾È ÇÊÅ͸µ ±â¹ý
¿µ¹®Á¦¸ñ(English Title)
Bayesian Filtering Method using n-gram Particle
ÀúÀÚ(Author)
ÀåÇÏ¿µ
À庴Ź
Ha-Young Jang
Byoung-Tak Zhang
¿ø¹®¼ö·Ïó(Citation)
VOL 40 NO. 05 PP. 0241 ~ 0247 (2013. 05)
Çѱ۳»¿ë
(Korean Abstract)
º£ÀÌÁö¾È ÇÊÅ͸µÀº °üÃøµ¥ÀÌÅÍ¿Í ¿¬°üµÈ È®·üÀ» ÀÌ¿ëÇÏ¿© °üÂûµÈ µ¥ÀÌÅ͸¦ ¼³¸íÇÒ ¼ö ÀÖ´Â Àº´Ð º¯¼ö¸¦ ¼³Á¤ÇÏ¿© ¸¶ÄÚÇÁ ¿¬¼â¸¦ µû¸£´Â Àº´Ð º¯¼öÀÇ °ªÀ» ÃßÁ¤ÇÏ´Â ¸ðµ¨·Î Ä®¸¸ ÇÊÅͳª ÆÄƼŬ ÇÊÅ͵îÀÇ ¹æ¹ýÀÌ ´ëÇ¥ÀûÀÌ´Ù. º» ³í¹®¿¡¼´Â ´ë´ã Çü½ÄÀ¸·Î ±¸¼ºµÈ µ¥ÀÌÅ͸¦ ÀÓÀǺ¸Çà È®·ü°úÁ¤À» µû¸£´Â ½Ã°è¿ µ¥ÀÌÅÍ·Î °£ÁÖÇÏ°í, À̾߱â È帧ÀÇ ÀüȯÀÌ ¸¶ÄÚÇÁ ¿¬¼â¿¡ ÀÇÇØ °áÁ¤µÈ´Ù°í °¡Á¤ÇÏ¿© º£ÀÌÁö¾È ÇÊÅ͸µ ±â¹ýÀ» ÀÌ¿ëÇÑ ´ëÈ ºÐ¼® ±â¹ýÀ» Á¦½ÃÇÏ¿´´Ù. Á¦¾ÈÇÑ ¹æ¹ý·ÐÀº n-gram ¾ð¾î¸ðµ¨°ú º£ÀÌÁö¾È ÇÊÅ͸µ ±â¹ýÀ» °áÇÕÇÏ¿© ¸»¹¶Ä¡·ÎºÎÅÍ n-gram ¾ð¾î¸ðµ¨À» ±¸ÃàÇÏ¿© À̸¦ Ãʱ⠺ÐÆ÷·Î ÀÌ¿ëÇÏ°í, À̸¦ ÆÄƼŬ·Î ÀÌ¿ëÇÏ¿© À̾߱â È帧ÀÇ ÀüȯÀ» ¿¹ÃøÇÏ°Ô µÈ´Ù. ÀϹÝÀûÀ¸·Î ¾ð¾î µ¥ÀÌÅÍ´Â ±× Ư¼º»ó º£ÀÌÁö¾È ÇÊÅ͸µ ±â¹ýÀÇ Æø³ÐÀº Àû¿ëÀÌ ¾î·Á¿îµ¥ º» ³í¹®¿¡¼ Á¦½ÃÇÑ n-gram ¾ð¾î¸ðµ¨°ú º£ÀÌÁö¾È ÇÊÅ͸µ ±â¹ýÀ» °áÇÕÀ» ÅëÇؼ ¾ð¾î󸮿¡ ÀÖ¾î¼ º£ÀÌÁö¾È ÇÊÅ͸µ ±â¹ýÀÇ º¸´Ù ³ÐÀº Àû¿ëÀÌ °¡´ÉÇÒ °ÍÀ¸·Î ±â´ëµÈ´Ù.
¿µ¹®³»¿ë
(English Abstract)
Bayesian filtering is a general probabilistic approach for estimating an unknown probability density function recursively over time using incoming measurements and a mathematical process model. Kalman filter and particle filters are the typical applications of it. We propose the n-gram filtering method to detect the conversational humor in spontaneous dialogue. This spontaneous dialogue is regarded as a kind of time series data which follows random walk process. The proposed method detects the conversational humor using the n-gram particle filter. The proposal distribution of n-gram particle filtering is selected by the n-gram language model. We expect that n-gram filtering methods provide very efficient way to use the sequential and temporal information in language data such as dialogue, storytelling and so on.
Å°¿öµå(Keyword)
º£ÀÌÁö¾È ÇÊÅ͸µ
ÆÄƼŬ ÇÊÅÍ
¾ð¾î ¸ðµ¨
À¯¸Ó ÀνÄ
½Ã°è¿ ¸ðµ¨
Bayesian filtering
particle filter
language model
humor detection
time series model
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