‫
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‫‪11th International CSI Computer Conference (CSICC’2006), School of Computer Science, IPM, Jan. 24-26, 2006, Tehran, Iran.‬‬
‫ﻃﺮﺍﺣﻲ ﻳﮏ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‬
‫ﺭﺿﺎ ﺣﺴﺎﻣﻲ ﻓﺮﺩ‬
‫ﻏﻼﻣﺮﺿﺎ ﻗﺎﺳﻢ ﺛﺎﻧﻲ‬
‫ﺩﺍﻧﺸﮕﺎﻩ ﺻﻨﻌﺘﻲ ﺷﺮﻳﻒ‬
‫ﺩﺍﻧﺸﮑﺪﻩ ﻣﻬﻨﺪﺳﻲ ﮐﺎﻣﭙﻴﻮﺗﺮ‬
‫ﺩﺍﻧﺸﮕﺎﻩ ﺻﻨﻌﺘﻲ ﺷﺮﻳﻒ‬
‫ﺩﺍﻧﺸﮑﺪﻩ ﻣﻬﻨﺪﺳﻲ ﮐﺎﻣﭙﻴﻮﺗﺮ‬
‫‪[email protected]‬‬
‫‪sani@ sharif.edu‬‬
‫ﭼﮑﻴﺪﻩ‪ :‬ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ ﺩﺭ ﺑـﺴﻴﺎﺭﻱ ﺍﺯ ﺯﻣﻴﻨـﻪ ﻫـﺎﻱ ﭘـﺮﺩﺍﺯﺵ ﺯﺑـﺎﻥ‬
‫ﻃﺒﻴﻌﻲ ﮐﺎﺭﺑﺮﺩﻫﺎﻱ ﮔﺴﺘﺮﺩﻩ ﺍﻱ ﺩﺍﺭﺩ‪ .‬ﻣﺘﻦ ﮐﺎﻭﻱ ﻭ ﺗﺮﺟﻤﻪ ﻣﺘﻮﻥ ﺍﺯ ﺭﻳـﺸﻪ‬
‫ﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ ﺑﺮﺍﻱ ﺑﻬﻴﻨﻪﮐﺮﺩﻥ ﺭﻭﺵﻫﺎﻱ ﺧﻮﺩ ﺑﻴﺸﺘﺮ ﺍﺳـﺘﻔﺎﺩﻩ ﮐـﺮﺩﻩ ﺍﻧـﺪ‪.‬‬
‫ﺭﻳﺨﺖ ﺷﻨﺎﺳﻲ ﺍﺯ ﻣﻬﻤﺘﺮﻳﻦ ﺍﺑﺰﺍﺭﻫﺎﻱ ﻣﻮﺭﺩ ﺍﺳﺘﻔﺎﺩﻩ ﺩﺭ ﺭﻳﺸﻪﻳﺎﺑﻲ ﮐﻠﻤـﺎﺕ‬
‫ﺍﺳﺖ‪ .‬ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﻳﮑﻲ ﺍﺯ ﻣﻬﻤﺘﺮﻳﻦ ﺭﻭﺵ ﻫﺎﻱ ﻣﺒﺘﻨﻲ ﺑﺮ ﺭﻳﺨﺖ ﺷﻨﺎﺳﻲ‬
‫ﺭﺍ ﺑﻬﻴﻨﻪ ﮐﺮﺩﻩ ﺍﻳﻢ‪ .‬ﻋﻼﻭﻩ ﺑﺮﺍﻳﻦ ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﻳﮏ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺭﻳﺸﻪﻳـﺎﺑﻲ‬
‫ﺭﺍ ﻣﻌﺮﻓﻲ ﺧﻮﺍﻫﻴﻢ ﮐﺮﺩ‪ .‬ﺑﺮﺍﻱ ﺍﻭﻟﻴﻦ ﺑﺎﺭ ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺍﺯ ﻧﻘﺶ ﮐﻠﻤﺎﺕ ﺑـﺮﺍﻱ‬
‫ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﺍﺳﺘﻔﺎﺩﻩ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫ﻭﺍﮊﻩ ﻫﺎﻱ ﻛﻠﻴﺪﻱ‪ :‬ﺭﻳﺸﻪ ﻳﺎﺑﻲ‪ ،‬ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‪ ،‬ﭘﺮﺩﺍﺯﺵ ﺯﺑﺎﻥ‪ ،‬ﺳﻴﺴﺘﻤﻬﺎﻱ‬
‫ﻣﺘﺮﺟﻢ‪.‬‬
‫‪ ‐١‬ﻣﻌﺮﻓﻲ‬
‫ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ‬
‫ﭘﺮﺩﺍﺯﺵ ﺯﺑﺎﻥ ﻃﺒﻴﻌﻲ ﺩﺭ ﺳﺎﻝ ﻫﺎﻱ ﺍﺧﻴﺮ ﺭﺷﺪ ﻗﺎﺑﻞ ﺗﻮﺟﻬﻲ ﺩﺍﺷﺘﻪ ﺍﺳـﺖ‪.‬‬
‫ﺭﻭﻳﮑﺮﺩﻫﺎﻱ ﺟﺪﻳﺪ ﺑﻪ ﺯﺑﺎﻥ ﻃﺒﻴﻌﻲ ﺑﻪ ﻋﻨﻮﺍﻥ ﺍﺑﺰﺍﺭﻱ ﺑﺮﺍﻱ ﺑﺮﻗﺮﺍﺭﻱ ﺍﺭﺗﺒـﺎﻁ‬
‫ﺑﺎ ﮐﺎﺭﺑﺮﺍﻥ ﺭﺍﻳﺎﻧﻪ ﺍﺯ ﻳﮏ ﺳﻮ ﻭ ﺍﻓﺰﺍﻳﺶ ﺣﺠـﻢ ﺍﻃﻼﻋـﺎﺕ ﻭ ﺩﺍﺩﻩ ﻫـﺎ ﻭ ﺭﺷـﺪ‬
‫ﺍﺑﺰﺍﺭﻫﺎﻱ ﭘﺮﺩﺍﺯﺵ ﻣﺘﻦ ﺍﺯ ﺳﻮﻱ ﺩﻳﮕـﺮ ﺭﺷـﺪ ﺳﻴـﺴﺘﻢ ﻫـﺎﻱ ﭘﺮﺩﺍﺯﺷـﻲ ﺭﺍ‬
‫ﺳﺒﺐ ﺷﺪﻩ ﺍﺳﺖ‪[۳].‬‬
‫ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺭﺷـﺪ ﭘـﺮﺩﺍﺯﺵ ﺯﺑـﺎﻥ ﻃﺒﻴﻌـﻲ ﮐﺎﺭﺑﺮﺩﻫـﺎﻱ‬
‫ﻓﺮﺍﻭﺍﻧﻲ ﭘﻴﺪﺍ ﮐﺮﺩﻩ ﺍﺳﺖ‪ .‬ﺑﻪ ﻃﻮﺭ ﮐﻠﻲ ﺩﻭ ﮐﺎﺭﺑﺮﺩ ﻋﻤﺪﻩ ﺑـﺮﺍﻱ ﺭﻳـﺸﻪ ﻳـﺎﺑﻲ‬
‫ﮐﻠﻤﺎﺕ ﻣﺮﺳﻮﻡ ﺍﺳﺖ‪:‬‬
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‫ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ ﺩﺭ ﻣﺎﺷﻴﻦ ﻫـﺎﻱ ﻣﺘـﺮﺟﻢ‪ :‬ﮐﻠﻤـﺎﺕ ﺑـﻪ‬
‫ﻫﻤﺮﺍﻩ ﺍﺷﺘﻘﺎﻕ ﻫﺎﻱ ﺁﻧﻬﺎ ﺗﻨﻮﻉ ﻣﺤﺴﻮﺳﻲ ﺑﻪ ﮐﻠﻤﺎﺕ ﻣﻲ ﺩﻫﻨـﺪ‬
‫ﻭ ﻋﻤﻼ ﺑﺎﻋﺚ ﺩﺷﻮﺍﺭﺗﺮ ﺷﺪﻥ ﺳﺎﺧﺘﺎﺭ ﻫﺎﻱ ﺗﺮﺟﻤـﻪﻱ‪ ،‬ﺟﻤـﻼﺕ‬
‫ﻣــﻲ ﺷــﻮﻧﺪ‪ .‬ﺑــﺎ ﺍﺳــﺘﻔﺎﺩﻩ ﺍﺯ ﺭﻳــﺸﻪ ﻳــﺎﺑﻲ ﮐﻠﻤــﺎﺕ ﺑــﻪ ﮐــﺎﻫﺶ‬
‫ﭘﻴﭽﻴﺪﮔﻲ ﻫﺎﻱ ﺗﺮﺟﻤﻪ ﻣﻲ ﭘﺮﺩﺍﺯﻧﺪ‪.‬‬
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‫ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ ﺩﺭ ﺳﻴﺴﺘﻢ ﻫﺎﻱ ﺑﺎﺯﻳـﺎﺑﻲ ﺍﻃﻼﻋـﺎﺕ‪:‬‬
‫ﺑﺎﺯﻳﺎﺑﻲ ﺍﻃﻼﻋﺎﺕ ﻭ ﭘﺮﺩﺍﺯﺵ ﻣﺘﻦ ﻳﮑﻲ ﺍﺯ ﮐﺎﺭﺑﺮﺩﻫﺎﻱ ﺭﻭ ﺑﻪ ﺭﺷﺪ‬
‫ﺩﻭﺭﻩ ﺍﺧﻴﺮ ) ﻋﺼﺮ ﻓـﻦﺁﻭﺭﻱ ﺍﻃﻼﻋـﺎﺕ( ﻣﺤـﺴﻮﺏ ﻣـﻲ ﮔـﺮﺩﺩ‪.‬‬
‫ﭘﺮﺩﺍﺯﺵ ﻭ ﺩﺳﺘﻪﺑﻨﺪﻱ ﺍﺧﺒـﺎﺭ‪ ،‬ﭘـﺮﺩﺍﺯﺵ ﻧﻮﺷـﺘﻪﻫـﺎﻱ ﻋﻠﻤـﻲ ﻭ‬
‫ﮐﺎﺭﺑﺮﺩﻫــﺎﻳﻲ ﺍﺯ ﺍﻳــﻦ ﺩﺳــﺖ ﺍﻣــﺮﻭﺯﻩ ﮐــﺎﻣﻼ ﻣﺮﺳــﻮﻡ ﺍﺳــﺖ‪ .‬ﺩﺭ‬
‫ﺳﻴﺴﺘﻢﻫﺎﻱ ﺑﺎﺯﻳﺎﺑﻲ ﺍﻃﻼﻋﺎﺕ ﻣﻌﻤﻮﻻ ﻳـﮏ ﭘﺎﻳﮕـﺎﻩ ﺩﺍﺩﻩ ﺑـﺴﻴﺎﺭ‬
‫ﺑﺰﺭﮒ ﻭﺟﻮﺩ ﺩﺍﺭﺩ ﮐﻪ ﺑﺎﻳﺪ ﭘﺮﺩﺍﺯﺵ ﻭ ﺑﺎﺯﻳﺎﺑﻲ ﺍﻃﻼﻋﺎﺕ ﺑـﺮ ﺭﻭﻱ‬
‫ﺁﻧﻬﺎ ﺻﻮﺭﺕ ﭘﺬﻳﺮﺩ‪ .‬ﻫﺮ ﭼﻪ ﺷﺒﮑﻪﻫﺎﻱ ﻣﻌﻨﺎﻳﻲ ﺍﺳﺘﺨﺮﺍﺝ ﺷﺪﻩ ﺍﺯ‬
‫ﺍﻳﻦ ﺍﻃﻼﻋﺎﺕ ﺩﻗﻴﻖﺗﺮ ﻭ ﮔﺴﺘﺮﺩﻩﺗﺮ ﺑﺎﺷﺪ‪ ،‬ﺍﻣﮑﺎﻥ ﻓـﺮﺍﻫﻢ ﺷـﺪﻥ‬
‫ﺍﻃﻼﻋــﺎﺕ ﺍﺳــﺘﺨﺮﺍﺝ ﺷــﺪﻩ ﺑﻴــﺸﺘﺮ‪ ،‬ﺭﺍﺣــﺖﺗــﺮ ﺍﺳــﺖ‪ .‬ﻳﮑــﻲ ﺍﺯ‬
‫ﮐﺎﺭﺑﺮﺩﻫﺎﻱ ﺭﻳـﺸﻪﻳـﺎﺑﻲ ﺩﺭ ﺍﻣﮑـﺎﻥ ﻓـﺮﺍﻫﻢ ﮐـﺮﺩﻥ ﺷـﺒﮑﻪﻫـﺎﻱ‬
‫ﻣﻌﻨﺎﻳﻲ ﮔﺴﺘﺮﺩﻩﺗﺮ ﺩﺭ ﺳﻴﺴﺘﻢﻫـﺎﻱ ﭘـﺮﺩﺍﺯﺵ ﻣـﺘﻦ ﻭ ﺑﺎﺯﻳـﺎﺑﻲ‬
‫ﺍﻃﻼﻋﺎﺕ ﺍﺳﺖ‪.‬‬
‫ﻋﻠﻲ ﺭﻗﻢ ﺁﻧﮑﻪ ﺻﻮﺭﺕ ﻣﺴﺎﻟﻪ ﺩﺭ ﺩﻭ ﮐﺎﺭﺑﺮﺩ ﺑـﺎﻻ ﺑـﺴﻴﺎﺭ ﻣـﺸﺎﺑﻪ ﺍﺳـﺖ‪ ،‬ﺍﻣـﺎ‬
‫ﺭﻳــﺸﻪﻳــﺎﺑﻲ ﮐﻠﻤــﺎﺕ ﺩﺭ ﺁﻧﻬــﺎ ﺩﺍﺭﺍﻱ ﻧﻴﺎﺯﻣﻨــﺪﻱﻫــﺎﻱ ﻣﺘﻔــﺎﻭﺗﻲ ﺍﺳــﺖ‪ .‬ﺩﺭ‬
‫ﺳﻴﺴﺘﻢﻫﺎﻱ ﻣﺘﺮﺟﻢ ﺑﻴﺸﺘﺮ ﺑـﻪ ﺩﻧﺒـﺎﻝ ﺭﻳـﺸﻪﻳـﺎﺑﻲ ﮐﻠﻤـﺎﺗﻲ ﻫـﺴﺘﻴﻢ ﮐـﻪ‬
‫ﺍﺷﺘﻘﺎﻕ ﮐﻠﻤﻪ ﺩﺭ ﻧﻮﻉ ﺁﻥ )ﻓﻌﻞ‪ ،‬ﺍﺳﻢ ﻭ ‪ (...‬ﺗﻐﻴﻴﺮﻱ ﺍﻳﺠﺎﺩ ﻧﻤـﻲﮐﻨـﺪ‪ .‬ﺯﻳـﺮﺍ‬
‫ﻫﻨﮕﺎﻣﻲ ﮐﻪ ﻧﻮﻉ ﮐﻠﻤﻪ‪ ،‬ﺗﻐﻴﻴﺮ ﻣﻲﮐﻨﺪ ﻣﻌﺎﺩﻝ ﺁﻥ ﺩﺭ ﺯﺑﺎﻥ ﻣﻘـﺼﺪ ﺗﻐﻴﻴـﺮﺍﺕ‬
‫ﻼ ﺗﺮﺟﻤﻪ ﺍﻧﺠﺎﻡ ﺷـﺪﻩ ﺍﺯ ﮐﻴﻔﻴـﺖ ﻣﻄﻠـﻮﺑﻲ ﺑﺮﺧـﻮﺭﺩﺍﺭ‬
‫ﺯﻳﺎﺩﻱ ﻣﻲﮐﻨﺪ ﻭ ﻋﻤ ﹰ‬
‫ﻧﻤﻲ ﺑﺎﺷﺪ‪ .‬ﺍﻳﻦ ﺩﺭ ﺣﺎﻟﻲ ﺍﺳﺖ ﮐﻪ ﺩﺭ ﺳﻴﺴﺘﻢ ﻫﺎﻱ ﭘﺮﺩﺍﺯﺵ ﻣـﺘﻦ‪ ،‬ﮐـﺸﻒ‬
‫ﺗﻤﺎﻣﻲ ﺍﺭﺗﺒﺎﻁ ﻫﺎ ﺑﺮﺍﻱ ﻣﺎ ﺍﻫﻤﻴﺖ ﺩﺍﺭﺩ‪ .‬ﺑﻪ ﻫﻤﻴﻦ ﺩﻟﻴﻞ ﺗﻐﻴﻴﺮ ﻧﻘﺶ ﻣـﺴﺎﻟﻪ‬
‫ﻣﻬﻤﻲ ﻧﻴﺴﺖ‪.‬‬
‫ﺑﻨﺎﺑﺮﺍﻳﻦ ﺩﺭ ﻣﺎﺷﻴﻦ ﻫﺎﻱ ﻣﺘﺮﺟﻢ ﺑﻴﺸﺘﺮ ﺑﺮ ﺭﻭﻱ ﻣﻮﺍﺭﺩﻱ ﺗﻤﺮﮐﺰ ﻣـﻲ ﺷـﻮﺩ‬
‫ﮐﻪ ﻧﻘﺶ ﮐﻠﻤﺎﺕ ﺩﺭ ﺁﻧﻬﺎ ﺗﻐﻴﻴﺮﻱ ﻧﻤﻲﮐﻨﺪ‪ .‬ﺻﺮﻑ ﺍﻓﻌﺎﻝ ﺍﺯ ﺍﻳﻦ ﺟﻤﻠﻪ ﺍﺳﺖ‪.‬‬
‫ﺍﻟﺒﺘﻪ ﺑﺎﻳﺪ ﺑﻪ ﻳﮏ ﻣﺴﺎﻟﻪ ﺍﺳﺎﺳﻲ ﺗﻮﺟﻪ ﮐﺮﺩ‪ .‬ﺍﻳﻦ ﻣﺴﺎﻟﻪ ﺍﺻﻼ ﺑﻪ ﺍﻳﻦ ﻣﻌﻨـﻲ‬
‫ﻧﻴﺴﺖ ﮐﻪ ﺭﻳﺸﻪﻳﺎﺑﻲ ﺩﺭ ﻣﻮﺍﺭﺩ ﺩﺭ ﺳﻴﺴﺘﻢ ﻫـﺎﻱ ﺗﺮﺟﻤـﻪ ﮐـﺎﺭﺍﻳﻲ ﻧﺪﺍﺭﻧـﺪ؛‬
‫ﺑﻠﮑﻪ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺍﺑﺰﺍﺭﻫﺎﻱ ﮐﻨﻮﻧﻲ ﺳﻴﺴﺘﻢ ﻫﺎﻱ ﺗﺮﺟﻤﻪ ﮐﻪ ﺑﻴـﺸﺘﺮ ﺩﺭ ﻻﻳـﻪ‬
‫ﺳﺎﺧﺘﺎﺭ ﮐﻠﻤﺎﺕ ﮐﺎﺭﺑﺮﺩ ﺩﺍﺭﻧﺪ ﻭ ﻧﻪ ﺩﺭ ﻻﻳﻪ ﻣﻔﻬﻮﻡ‪ ،‬ﺑﺎﻳﺪ ﺭﻳﺸﻪﻳـﺎﺑﻲ ﺭﺍ ﺗﻨﻬـﺎ‬
‫ﺩﺭ ﻫﻤﻴﻦ ﻣﺴﻴﺮ ﻫﺪﺍﻳﺖ ﮐﺮﺩ]‪.[۲،۳‬‬
‫ﮐﻠﻤﺎﺕ‪ ،‬ﺩﺭ ﻫﺮ ﺯﺑﺎﻥ ﺑـﻪ ﺩﻭ ﺩﺳـﺘﻪ ﺟﺎﻣـﺪ ﻭ ﻣﺮﮐـﺐ ﺗﻘـﺴﻴﻢ ﻣﻴـﺸﻮﻧﺪ‪ .‬ﺑـﻪ‬
‫ﮐﻠﻤﺎﺗﻲ ﮐﻪ ﺍﺯ ﺩﻳﮕﺮ ﮐﻠﻤﺎﺕ ﻣﺸﺘﻖ ﺷﺪﻩﺍﻧﺪ ﮐﻠﻤﺎﺕ ﻣﺮﮐﺐ ﮔﻔﺘﻪ ﻣﻲ ﺷـﻮﺩ‪.‬‬
‫ﮐﻠﻤﺎﺕ ﺟﺎﻣﺪ ﮐﻠﻤﺎﺗﻲ ﻫﺴﺘﻨﺪ ﮐﻪ ﺩﺭ ﺯﺑﺎﻥ ﺍﺯ ﻫﻴﭻ ﮐﻠﻤﻪﺍﻱ ﻣﺸﺘﻖ ﻧﺸﺪﻩﺍﻧﺪ‪.‬‬
‫ﻳﺎﻓﺘﻦ ﺭﻳﺸﻪ ﮐﻠﻤﺎﺕ ﻣﺮﮐﺐ ﺭﺍ ﺍﺻﻄﻼﺣﺎ ﺭﻳﺸﻪﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ ﻣﻲ ﮔـﻮﻳﻴﻢ‪ .‬ﺑـﻪ‬
‫ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ "ﺁﻣﻮﺯﮔﺎﺭ" ﮐﻠﻤﻪ ﻣﺮﮐﺒﻲ ﺍﺳـﺖ ﮐـﻪ ﺍﺯ ﺗﺮﮐﻴـﺐ‬
‫"ﺁﻣﻮﺯ" ﻭ "ﮔﺎﺭ" ﺗﺸﮑﻴﻞ ﺷﺪﻩ ﺍﺳﺖ؛ ﻳـﺎ ﺩﺭ ﺯﺑـﺎﻥ ﺍﻧﮕﻠﻴـﺴﻲ ’‪ ‘teacher‬ﺑـﺎ‬
‫ﮐﻠﻤﺎﺕ ’‪ ‘teach‬ﻭ ’‪ ‘er‬ﺳﺎﺧﺘﻪ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫‪ ‐٢‬ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﺭﻳﺸﻪﻳﺎﺑﻲ‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﭘﺲﻭﻧﺪ ﻭ ﭘﻴﺶﻭﻧﺪ ﮐﻠﻤﺎﺕ ﺭﺍ ﺑﺮﺭﺳﻲ ﻣﻲ ﮐﻨﺪ ﻭ ﺩﺭ‬
‫ﻣﺎﺷﻴﻦ ﻫﺎﻱ ﻣﺘﺮﺟﻢ ﮐﺎﺭﺍﻳﻲ ﺧﻮﺑﻲ ﺭﺍ ﻧﺸﺎﻥ ﺩﺍﺩﻩ ﺍﺳﺖ‪.‬‬
‫ﺗﺎﮐﻨﻮﻥ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﺯﻳﺎﺩﻱ ﺑﺮﺍﻱ ﺭﻳﺸﻪﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ ﻣﻌﺮﻓﻲ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺑﺎ‬
‫ﺍﻳﻦ ﺣﺎﻝ ﺩﺭ ﻳﮏ ﺩﺳﺘﻪ ﺑﻨﺪﻱ ﮐﻠﻲ ﻣﻲ ﺗﻮﺍﻥ ﺩﻭ ﺍﻳﺪﻩ ﺍﺻﻠﻲ ﺭﺍ ﺩﺭ ﺁﻧﻬﺎ‬
‫ﺟﺴﺘﺠﻮ ﮐﺮﺩ‪:‬‬
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‫ﺷﺒﮑﻪ ﻫﺎﻱ ﮐﻠﻤﺎﺕ‪ :١‬ﺩﺭ ﺍﻳﻦ ﺭﻭﺵﻫﺎ ﺍﺯ ﺍﺭﺗﺒﺎﻁ ﮐﻠﻤﺎﺕ ﺑﺎ ﻫﻢ‬
‫ﺩﺭ ﻳﮏ ﺷﺒﮑﻪ ﻣﻌﻨﺎﻳﻲ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲ ﺷﻮﺩ‪ .‬ﮐﻠﻤﺎﺗﻲ ﮐﻪ ﺩﺍﺭﺍﻱ‬
‫ﻳﮏ ﺭﻳﺸﻪ ﻫﺴﺘﻨﺪ ﻣﺸﺨﺺ ﺷﺪﻩ ﻭ ﺩﺭ ﻳﮏ ﺷﺒﮑﻪ ﮔﺮﺍﻑ ﻣﺎﻧﻨﺪ‬
‫ﺑﻪﻃﻮﺭﻱ ﮐﻪ ﺍﻋﻀﺎﻱ ﻳﮏ ﺩﺳﺘﻪ ﺗﺸﮑﻴﻞ ﻳﮏ ﺧﻮﺷﻪ ﺭﺍ ﺩﺍﺩﻩﺍﻧﺪ‪،‬‬
‫ﻧﮕﻪﺩﺍﺭﻱ ﻣﻲﺷﻮﺩ‪ .‬ﺍﻳﻦ ﺭﻭﺵﻫﺎ ﺍﺣﺘﻴﺎﺝ ﺑﻪ ﻧﻈﺎﺭﺕ ﺑﺴﻴﺎﺭ ﺯﻳﺎﺩ‬
‫ﻋﺎﻣﻞ ﺍﻧﺴﺎﻧﻲ ﺩﺍﺭﻧﺪ‪ .‬ﺯﻳﺮﺍ ﻫﻨﻮﺯ ﺍﻟﮕﻮﺭﻳﺘﻤﻬﺎﻳﻲ ﮐﻪ ﺑﺘﻮﺍﻥ ﺑﺎ‬
‫ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺁﻧﻬﺎ ﺷﺒﮑﻪﻫﺎﻱ ﻗﺎﺑﻞ ﺍﻃﻤﻴﻨﺎﻥ ﻳﺎﻓﺖ ﻭﺟﻮﺩ ﻧﺪﺍﺭﺩ‪ .‬ﺍﻣﺎ‬
‫ﺩﺭ ﻣﻘﺎﺑﻞ ﺩﺍﻣﻨﻪ ﮐﺎﺭﺍﻳﻲ ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎ ﺑﺴﻴﺎﺭ ﮔﺴﺘﺮﺩﻩ ﺍﺳﺖ ﻭ‬
‫ﺩﺭ ﺳﻴﺴﺘﻢﻫﺎﻱ ﺩﺍﺩﻩ ﮐﺎﻭﻱ ﻭ ﻣﺘﻦ ﮐﺎﻭﻱ ﮐﺎﺭﺍﻳﻲ ﻣﻨﺎﺳﺒﻲ‬
‫ﺩﺍﺭﻧﺪ‪ .‬ﻧﻤﻮﻧﻪﺍﻱ ﺍﺯ ﺳﻴﺴﺘﻢﻫﺎﻱ ﻣﻮﻓﻖ ﺭﻳﺸﻪﻳﺎﺑﻲ ﺑﺎ ﺍﻳﻦ ﺭﻭﻳﮑﺮﺩ‬
‫ﺩﺭ ]‪ [۵‬ﺩﻳﺪﻩ ﻣﻲﺷﻮﺩ‪.‬‬
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‫ﺭﻳﺨﺖ ﺷﻨﺎﺳﻲ‪ :٢‬ﺩﺭ ﺭﻭﻳﮑﺮﺩ ﺩﻭﻣﻲ ﮐﻪ ﺑﻪ ﻣﺴﺎﻟﻪ ﺭﻳﺸﻪ ﻳﺎﺑﻲ‬
‫ﻭﺟﻮﺩ ﺩﺍﺭﺩ ﺍﺯ ﻗﻮﺍﻧﻴﻦ ﺳﺎﺧﺖ ﮐﻠﻤﺎﺕ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲﺷﻮﺩ‪ .‬ﺍﻳﻦ‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎ ﺑﺎ ﺑﺮﺭﺳﻲ ﺭﻭﺵﻫﺎﻱ ﺳﺎﺧﺖ ﮐﻠﻤﺎﺕ ﺭﻳﺸﻪ ﮐﻠﻤﺎﺕ‬
‫ﺭﺍ ﭘﻴﺪﺍ ﻣﻲﮐﻨﻨﺪ‪ .‬ﺍﻳﻦ ﺭﻭﺵﻫﺎ ﺑﻪ ﻋﻠﺖ ﻣﺸﺨﺺ ﺑﻮﺩﻥ ﺭﻭﺵﻫﺎﻱ‬
‫ﺳﺎﺧﺖ ﮐﻠﻤﺎﺕ ﺍﻣﮑﺎﻥ ﻣﮑﺎﻧﻴﺰﻩﺷﺪﻥ ﺧﻮﺑﻲ ﺭﺍ ﻓﺮﺍﻫﻢ ﮐﺮﺩﻩﺍﻧﺪ‪.‬‬
‫ﺍﻣﺎ ﻣﺸﮑﻞ ﺍﺻﻠﻲ ﺩﺭ ﺁﻧﻬﺎ ﻫﻨﮕﺎﻣﻲ ﺭﺥ ﻣﻲ ﺩﻫﺪ ﮐﻪ ﺩﻭ ﮐﻠﻤﻪ‬
‫ﺟﺎﻣﺪ ﺑﺎ ﺩﻭ ﺗﻐﻴﻴﺮ ﻣﺨﺘﻠﻒ ﻳﮏ ﮐﻠﻤﻪ ﻣﺮﮐﺐ ﻳﮑﺴﺎﻥ ﻣﻲ ﺳﺎﺯﻧﺪ‪.‬‬
‫ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﮐﻠﻤﻪ ‪ goes‬ﻣﻲ ﺗﻮﺍﻧﺪ ﺑﻪ ﻣﻌﻨﻲ ‪ goe + s‬ﺑﻪ‬
‫ﻣﻌﻨﻲ ﺯﻧﺒﻮﺭﻫﺎﻱ ﻋﺴﻞ ﺑﺎﺷﺪ ﻭ ﻳﺎ ‪ go + es‬ﺑﻪ ﻣﻌﻨﻲ ﺻﻮﺭﺗﻲ‬
‫ﺍﺯﻓﻌﻞ ﺭﻓﺘﻦ‪ .‬ﻧﻤﻮﻧﻪﻫﺎﻳﻲ ﺍﺯ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﺭﻳﺨﺖ ﺷﻨﺎﺳﻲ ]‪[۱،۴‬‬
‫ﻫﺴﺘﻨﺪ‪.‬‬
‫ﺍﻭﻟﻴﻦ ﮐﺎﺭﺑﺮﺩ ﺍﻳﻦ ﺍﻳﺪﻩ ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﺍﻧﮕﻠﻴﺴﻲ ﻣﻌﺮﻓﻲ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺻﺮﻑ ﺍﻓﻌﺎﻝ‬
‫ﺯﺑﺎﻥ ﺍﻧﮕﻠﻴﺴﻲ ﻭ ﻫﻤﭽﻨﻴﻦ ﺍﻓﺰﻭﺩﻥ ‪ s/es‬ﺑﻪ ﺍﻧﺘﻬﺎﻱ ﺍﺳﺎﻣﻲ ﺑﺮﺍﻱ ﺟﻤﻊ‬
‫ﺑﺴﺘﻦ ﺁﻧﻬﺎ ﻣﻮﺭﺩ ﺑﺮﺭﺳﻲ ﻗﺮﺍﺭ ﻣﻲﮔﻴﺮﺩ‪ .‬ﻫﻤﺎﻥ ﻃﻮﺭ ﮐﻪ ﻣﻼﺣﻈﻪ ﻣﻲﺷﻮﺩ‬
‫ﺍﻳﻦ ﺗﻐﻴﻴﺮﺍﺕ ﻧﻘﺶ ﮐﻠﻤﺎﺕ ﺭﺍ ﺗﻐﻴﻴﺮ ﻧﻤﻲ ﺩﻫﻨﺪ‪ ،‬ﺑﻪ ﻫﻤﻴﻦ ﺩﻟﻴﻞ ﺑﺮﺍﻱ‬
‫ﻣﺎﺷﻴﻦ ﻫﺎﻱ ﻣﺘﺮﺟﻢ ﺑﺴﻴﺎﺭ ﻣﻨﺎﺳﺐ ﻫﺴﺘﻨﺪ‪ .‬ﺍﻟﺒﺘﻪ ﺑﺴﻴﺎﺭﻱ ﺍﺯ ﺩﻳﮕﺮ‬
‫ﺗﻐﻴﻴﺮﺍﺗﻲ ﮐﻪ ﺑﻪ ﺻﻮﺭﺕ ﻗﺎﻧﻮﻥﻣﻨﺪ ﺩﺭ ﮐﻠﻤﺎﺕ ﺍﻳﺠﺎﺩ ﻣﻲ ﺷﻮﻧﺪ‪ ،‬ﻣﺎﻧﻨﺪ‬
‫ﻗﻴﺪﻫﺎﻳﻲ ﮐﻪ ﺑﺎ ﺍﻓﺰﻭﺩﻥ ‪ ly‬ﺑﻪ ﺻﻔﺎﺕ ﺳﺎﺧﺘﻪ ﻣﻲﺷﻮﻧﺪ‪ ،‬ﻫﻢ ﻗﺎﺑﻞ ﺗﺸﺨﻴﺺ‬
‫ﺗﻮﺳﻂ ﺍﻳﺪﻩ ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ ﻫﺴﺘﻨﺪ‪.‬‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺗﻌﺪﺍﺩﻱ ﺍﺯ ﭘﺲﻭﻧﺪﻫﺎ ﺭﺍ ﻣﻮﺭﺩ ﺑﺮﺭﺳﻲ ﻗﺮﺍﺭ ﻣﻲ ﺩﻫﺪ ﻭ ﺩﺭ‬
‫ﻫﺮ ﻣﺮﺣﻠﻪ ﺑﺎ ﺯﺩﻭﺩﻥ ﭘﺴﻮﻧﺪ ﻳﺎﻓﺖ ﺷﺪﻩ‪ ،‬ﺭﻳﺸﻪ ﺑﺪﺳﺖ ﺁﻣﺪﻩ ﺭﺍ ﻣﻮﺭﺩ ﺑﺮﺭﺳﻲ‬
‫ﻗﺮﺍﺭ ﻣﻲ ﺩﻫﺪ‪ .‬ﺩﺭ ﺻﻮﺭﺗﻲ ﮐﻪ ﺍﻳﻦ ﺭﻳﺸﻪ ﺩﺭ ﻓﺮﻫﻨﮓ ﻟﻐﺖ ﻣﻮﺟﻮﺩ ﺑﺎﺷﺪ‪،‬‬
‫ﻋﻤﻞ ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﻣﺘﻮﻗﻒ ﻣﻲ ﺷﻮﺩ‪ .‬ﮔﺎﻫﻲ ﺍﻓﺰﻭﺩﻥ ﻳﮏ ﭘﺴﻮﻧﺪ ﺑﺎﻋﺚ‬
‫ﺗﻐﻴﻴﺮﺍﺗﻲ ﺩﺭ ﺳﺎﺧﺘﺎﺭ ﻳﮏ ﮐﻠﻤﻪ ﻣﻲ ﺷﻮﺩ ﻭ ﻫﻤﭽﻨﻴﻦ ﮔﺎﻫﻲ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ‬
‫ﺳﺎﺧﺘﺎﺭ ﮐﻠﻤﻪ ﺍﻳﻦ ﭘﺴﻮﻧﺪﻫﺎ ﺗﻐﻴﻴﺮ ﺩﺍﺩﻩ ﻣﻲﺷﻮﺩ‪ .‬ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﺍﮔﺮ ﺩﺭ‬
‫ﺍﻧﺘﻬﺎﻱ ﻳﮏ ﻓﻌﻞ ﺩﺭ ﺯﺑﺎﻥ ﺍﻧﮕﻠﻴﺴﻲ ’‪ ‘o‬ﻗﺮﺍﺭ ﺩﺍﺷﺘﻪ ﺑﺎﺷﺪ ﺑﻪ ﺟﺎﻱ ‪ s‬ﺑﺎﻳﺪ‬
‫ﭘﺴﻮﻧﺪ ‪ es‬ﺭﺍ ﺑﻪ ﺍﻧﺘﻬﺎﻱ ﺁﻥ ﺍﻓﺰﻭﺩ‪ .‬ﺑﻪ ﻫﻤﻴﻦ ﺩﻟﻴﻞ ﻭ ﺑﺮﺍﻱ ﮐﺸﻒ ﭼﻨﻴﻦ‬
‫ﮐﻠﻤﺎﺗﻲ ﺩﺭ ﺻﻮﺭﺗﻲ ﮐﻪ ﺭﻳﺸﻪ ﮐﻠﻤﻪ ﺩﺭ ﻓﺮﻫﻨﮓ ﻟﻐﺖ ﻧﺒﺎﺷﺪ‪ ،‬ﺑﺮ ﺭﻭﻱ ﮐﻠﻤﻪ‬
‫ﻳﮏ ﺳﺮﻱ ﺗﻐﻴﻴﺮﺍﺗﻲ ﺍﻋﻤﺎﻝ ﻣﻲ ﮐﻨﺪ ﺗﺎ ﻫﻨﮕﺎﻣﻲ ﮐﻪ ﺭﻳﺸﻪ ﺩﺭ ﻓﺮﻫﻨﮓ ﻟﻐﺖ‬
‫ﻳﺎﻓﺖ ﺷﻮﺩ‪.‬‬
‫ﺑﺎ ﺍﻳﻦ ﺣﺎﻝ ﺩﺭ ﻣﻮﺍﺭﺩ ﺑﺴﻴﺎﺭﻱ ﺩﻳﺪﻩ ﻣﻲﺷﻮﺩ ﮐﻪ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﻧﺘﺎﻳﺞ‬
‫ﺩﺭﺳﺘﻲ ﺭﺍ ﻧﺸﺎﻥ ﻧﻤﻲﺩﻫﺪ‪ .‬ﺟﺪﻭﻝ ‪ ۱‬ﺑﺮﺧﻲ ﺍﺯ ﺁﻧﻬﺎ ﺭﺍ ﻧﺸﺎﻥ ﻣﻲﺩﻫﺪ‪ .‬ﺑﻪ‬
‫ﻫﻤﻴﻦ ﺩﻟﻴﻞ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‪ ۲‬ﺭﺍ ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﺍﻧﮕﻠﻴﺴﻲ ﻣﻌﺮﻓﻲ ﻣﻲﮐﻨﻴﻢ ﮐﻪ‬
‫ﺍﻳﻦ ﻣﺸﮑﻼﺕ ﺭﺍ ﺑﺮﻃﺮﻑ ﮐﺮﺩﻩ ﺍﺳﺖ‪.‬‬
‫ﺟﺪﻭﻝ ‪۱‬ﺑﺮﺧﻲ ﺍﺯ ﻣﺜﺎﻝ ﻫﺎﻳﻲ ﮐﻪ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﭘﺎﺳﺦ ﻧﺎﺩﺭﺳﺖ ﺑﻪ ﺁﻧﻬﺎ ﺩﺍﺩﻩ‬
‫ﺍﺳﺖ‪.‬‬
‫ﺩﺭ ﺍﻳﻦ ﺑﺨﺶ ﻣﺎ ﻣﻲﺧﻮﺍﻫﻴﻢ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺭﻳﺸﻪﻳﺎﺑﻲ ﮐﻪ ﺍﺯ ﻧﻘﺶ ﮐﻠﻤﺎﺕ ﺑﺮﺍﻱ‬
‫ﺭﻳﺸﻪﻳﺎﺑﻲ ﺍﺳﺘﻔﺎﺩﻩ ﮐﺮﺩﻩ ﺍﺳﺖ ﺭﺍ ﻣﻌﺮﻓﻲ ﮐﻨﻴﻢ‪ .‬ﺍﺑﺘﺪﺍ ﺑﻪ ﺗﺸﺮﻳﺢ ﺍﻟﮕﻮﺭﻳﺘﻢ‬
‫ﮐﺮﺍﻭﺗﺰ‪ ٣‬ﻣﻲ ﭘﺮﺩﺍﺯﻳﻢ ]‪.[۱‬‬
‫‪goe‬‬
‫‪go‬‬
‫‪going‬‬
‫‪ ۱‐۲‬ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‬
‫‪bee‬‬
‫‪be‬‬
‫‪being‬‬
‫‪Find stem‬‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺍﻭﻟﻴﻦ ﺑﺎﺭ ﺩﺭ ﺳﺎﻝ ‪ ۱۹۹۳‬ﻣﻌﺮﻓﻲ ﺷﺪ]‪ .[۱‬ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ‬
‫ﮐﻪ ﺍﺯ ﺭﻭﺵﻫﺎﻱ ﺭﻳﺨﺖ ﺷﻨﺎﺳﻲ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲ ﮐﻨﺪ ﻭ ﺍﺯ ﻳﮏ ﻓﺮﻫﻨﮓ ﻟﻐﺖ‬
‫ﺑﺮﺍﻱ ﺁﺯﻣﻮﺩﻥ ﺭﻳﺸﻪ ﻫﺎﻱ ﻳﺎﻓﺖ ﺷﺪﻩ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲﮐﻨﺪ‪ .‬ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺑﺮﺍﻱ‬
‫ﺯﺑﺎﻥﻫﺎﻳﻲ ﮐﻪ ﺳﺎﺧﺖ ﮐﻠﻤﺎﺕ ﻣﺮﮐﺐ ﺩﺭ ﺁﻧﻬﺎ ﻗﺎﻧﻮﻥﻣﻨﺪ ﺍﺳﺖ‪ ،‬ﮐﺎﺭﺍﻳﻲ ﺧﻮﺑﻲ‬
‫ﺭﺍ ﻧﺸﺎﻥ ﺩﺍﺩﻩ ﺍﺳﺖ‪ .‬ﺯﺑﺎﻥﻫﺎﻱ ﻣﺠﺎﺭﻱ ﻭ ﻋﺒﺮﻱ ﺍﺯ ﺍﻳﻦ ﺩﺳﺘﻪ ﻫﺴﺘﻨﺪ‪.‬‬
‫‪word net‬‬
‫‪1‬‬
‫‪Morphology‬‬
‫‪2‬‬
‫‪Krovetz‬‬
‫‪3‬‬
‫‪۲‐۲‬‬
‫‪Main stem‬‬
‫‪word‬‬
‫ﺍﻟﮕﻮﺭﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‪۲‬‬
‫ﺩﺭ ﻧﺴﺨﻪ ﺑﻬﺒﻮﺩ ﻳﺎﻓﺘﻪ ﺍﺯ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‪ ۲‬ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻧﻘﺶ ﮐﻠﻤﺎﺕ ﺩﺭ‬
‫ﺟﻤﻼﺕ ﺑﺨﺶ ﻗﺎﺑﻞ ﺗﻮﺟﻬﻲ ﺍﺯ ﺍﺷﺘﺒﺎﻫﺎﺕ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺭﺍ ﺑﺮﻃﺮﻑ‬
‫ﮐﺮﺩﻩﺍﻳﻢ‪ .‬ﺩﺭ ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺍﺯ ﻧﻘﺶ ﮐﻠﻤﺎﺕ ﻋﻼﻭﻩ ﺑﺮ ﻭﺟﻮﺩ ﻳﺎ ﻋﺪﻡ ﻭﺟﻮﺩ‬
‫ﺁﻧﻬﺎ ﺩﺭ ﻓﺮﻫﻨﮓ ﻟﻐﺖ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲ ﮐﻨﺪ‪ .‬ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﺩﺭ ﻓﺮﺁﻳﻨﺪ ﺭﻳﺸﻪ‬
‫ﻳﺎﺑﻲ ﮐﻠﻤﻪ ‪ being‬ﺍﺑﺘﺪﺍ ‪ bee‬ﺟﺴﺘﺠﻮ ﻣﻲ ﺷﻮﺩ ﮐﻪ ﺩﺭ ﻓﺮﻫﻨﮓ ﻟﻐﺖ ﻫﻢ‬
‫ﻣﻮﺟﻮﺩ ﻣﻲﺑﺎﺷﺪ‪ .‬ﺑﻨﺎﺑﺮﺍﻳﻦ ﺑﻪ ﻋﻨﻮﺍﻥ ﺭﻳﺸﻪ ﮐﻠﻤﻪ ﺑﺮﮔﺮﺩﺍﻧﺪﻩ ﻣﻲ ﺷﺪ‪ .‬ﺍﻣﺎ ﺑﺎ‬
‫ﺗﻮﺟﻪ ﺑﻪ ﺍﻳﻨﮑﻪ ‪ bee‬ﻳﮏ ﺍﺳﻢ ﺍﺳﺖ ﻧﻤﻲﺗﻮﺍﻥ ﺑﻪ ﺁﻥ ﭘﺴﻮﻧﺪ ‪ ing‬ﺍﻓﺰﻭﺩ‬
‫ﺑﻨﺎﺑﺮﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺍﻳﻦ ﺭﻳﺸﻪ ﺭﺍ ﺑﻪ ﻋﻨﻮﺍﻥ ﺭﻳﺸﻪ ﺍﺻﻠﻲ ﺍﻧﺘﺨﺎﺏ ﻧﻤﻲﮐﻨﺪ ﻭ‬
‫ﺑﻪ ﺍﻳﻦ ﺗﺮﺗﻴﺐ ﺑﺎ ﺍﺩﺍﻣﻪ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺑﻪ ﺭﻳﺸﻪ ﺩﺭﺳﺖ ﮐﻠﻤﻪ ﮐﻪ ﻫﻤﺎﻥ ‪ be‬ﺍﺳﺖ‬
‫ﻣﻲﺭﺳﻴﻢ‪ .‬ﺍﺯ ﺁﻧﺠﺎ ﮐﻪ ﻫﺪﻑ ﻣﺎ ﺑﺮﺭﺳﻲ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‬
‫ﺍﺳﺖ ﺑﺮﺭﺳﻲ ﺟﺰﻳﻴﺎﺕ ﺭﺍ ﺑﻪ ﺑﺨﺶ ﺑﻌﺪ ﻣﻮﮐﻮﻝ ﻣﻲﮐﻨﻴﻢ‪.‬‬
‫‪ ‐٣‬ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﮐﻠﻤﺎﺕ ﻓﺎﺭﺳﻲ‬
‫ﺑﺮﺍﻱ ﺭﻳﺸﻪﻳﺎﺑﻲ ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﺗﻼﺵﻫﺎﻱ ﺟﺪﻱ ﭼﻨﺪﺍﻧﻲ ﻧﺸﺪﻩ ﺍﺳﺖ‪ .‬ﺩﺭ‬
‫]‪ [۶‬ﺍﻟﮕﻮﺭﻳﺘﻢ ﻫﺎﻱ ﺧﻮﺑﻲ ﺍﺭﺍﺋﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺍﻳﻦ ﺩﺭ ﺣﺎﻟﻲ ﺍﺳﺖ ﮐﻪ ﺩﺭ ﺯﺑﺎﻥ‬
‫ﻓﺎﺭﺳﻲ ﺑﻪ ﻋﻠﺖ ﺭﻭﺵﻣﻨﺪ ﺑﻮﺩﻥ ﺍﺷﺘﻘﺎﻕ ﮐﻠﻤﺎﺕ‪ ،‬ﺭﻳﺸﻪﻳﺎﺑﻲ ﺍﺯ ﺍﻣﮑﺎﻥ‬
‫ﻣﮑﺎﻧﻴﺰﻩ ﺷﺪﻥ ﺧﻮﺑﻲ ﺑﺮﺧﻮﺭﺩﺍﺭ ﺍﺳﺖ‪ .‬ﻫﻤﺎﻥ ﻃﻮﺭ ﮐﻪ ﮔﻔﺘﻪ ﺷﺪ ﺩﺭ ﺯﺑﺎﻥ‬
‫ﻫﺎﻳﻲ ﮐﻪ ﺩﺍﺭﺍﻱ ﺍﺷﺘﻘﺎﻕ ﻫﺎﻱ ﺭﻳﺨﺘﻲ )‪ (morphological‬ﺯﻳﺎﺩﺗﺮﻱ‬
‫ﻫﺴﺘﻨﺪ ﻗﺎﺑﻠﻴﺖ ﻫﺎﻱ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺑﻬﺘﺮ ﺩﻳﺪﻩ ﻣﻲ ﺷﻮﺩ‪ .‬ﻓﺎﺭﺳﻲ ﻭ‬
‫ﻋﺮﺑﻲ ﺍﺯ ﺍﻳﻦ ﺩﺳﺘﻪ ﻫﺴﺘﻨﺪ‪ .‬ﺭﻭﺵ ﻫﺎﻱ ﻣﺸﺎﺑﻪ ﺗﻨﻬﺎ ﺍﺯ ﺳﺎﺧﺘﺎﺭ ﻫﺎﻱ ﺯﺑﺎﻧﻲ‬
‫ﺍﺳﺘﻔﺎﺩﻩ ﮐﺮﺩﻩﺍﻧﺪ ﮐﻪ ﺩﺭ ﻧﺘﻴﺠﻪ ﻧﺘﺎﻳﺞ ﺁﻧﻬﺎ ﻗﺎﺑﻞ ﺑﻬﺘﺮ ﺷﺪﻥ ﻣﻲ ﺑﺎﺷﺪ‪ .‬ﺑﺮﺍﻱ‬
‫ﺭﻳﺸﻪﻳﺎﺑﻲ ﺍﻓﻌﺎﻝ ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﮐﺮﺍﻭﺗﺰ ﻭ ﮐﺮﺍﻭﺗﺰ‪ ۲‬ﺭﺍ‬
‫ﮔﺴﺘﺮﺵ ﺩﺍﺩﻩﺍﻳﻢ‪.‬‬
‫‪١‐٣‬‬
‫ﻓﻌﻞ‬
‫ﻃﺮﻳﻘﻪ ﺳﺎﺧﺖ‬
‫ﻣﺎﺿﻲ ﺳﺎﺩﻩ‬
‫ﺑﻦ ﻣﺎﺿﻲ ‪}+‬ﻡ‪,‬ﻱ؛ﻳﻢ‪,‬ﺗﺪ‪,‬ﻧﺪ(‬
‫ﻣﺎﺿﻲ ﺍﺳﺘﻤﺮﺍﺭﻱ‬
‫ﻣﻲ ‪ +‬ﻣﺎﺿﻲ ﺳﺎﺩﻩ‬
‫ﻣﺎﺿﻲ ﻧﻘﻠﻲ‬
‫ﺑﻦ ﻣﺎﺿﻲ ‪ +‬ﻩ ‪} +‬ﺍﻡ ‪,‬ﺍﻱ ‪,‬ﺍﺳﺖ ‪,‬ﺍﻳﻢ ‪,‬ﺍﻳﺪ ‪,‬ﺍﻧﺪ {‬
‫ﻣﺎﺿﻲ ﺑﻌﻴﺪ‬
‫ﺑﻦ ﻣﺎﺿﻲ‪+‬ﻩ‪+‬ﺑﻮﺩ‪}+‬ﻡ‪,‬ﻱ‪,‬ﻳﻢ‪,‬ﻳﺪ‪,‬ﻧﺪ{‬
‫ﻣﺎﺿﻲ ﺍﻟﺘﺰﺍﻣﻲ‬
‫ﺑﻦ ﻣﺎﺿﻲ ‪+‬ﻩ‪+‬ﺑﺎﺵ‪} +‬ﻡ‪,‬ﻱ‪,‬ﻳﻢ‪,‬ﻳﺪ‪,‬ﻧﺪ{‬
‫ﻣﻀﺎﺭﻉ ﺍﺧﺒﺎﺭﻱ‬
‫ﻣﻲ ‪ +‬ﺑﻦ ﻣﻀﺎﺭﻉ ‪} +‬ﻡ‪,‬ﻱ‪,‬ﺩ‪,‬ﻳﻢ‪,‬ﻳﺪ‪,‬ﻧﺪ{‬
‫ﻣﻀﺎﺭﻉ ﺳﺎﺩﻩ‬
‫ﺏ ‪ +‬ﺑﻦ ﻣﻀﺎﺭﻉ ‪} +‬ﻡ‪,‬ﻱ‪,‬ﺩ‪,‬ﻳﻢ‪,‬ﻳﺪ‪,‬ﻧﺪ{‬
‫ﻣﻀﺎﺭﻉ ﺳﺎﺩﻩﻣﻨﻔﻲ‬
‫ﻥ ‪ +‬ﺑﻦ ﻣﻀﺎﺭﻉ ‪} +‬ﻡ‪,‬ﻱ‪,‬ﺩ‪,‬ﻳﻢ‪,‬ﻳﺪ‪,‬ﻧﺪ{‬
‫ﻓﻌﻞ ﺍﻣﺮ‬
‫ﺏ ‪ +‬ﺑﻦ ﻣﻀﺎﺭﻉ ‪,""}+‬ﻳﻢ‪,‬ﻳﺪ{‬
‫ﻓﻌﻞ ﺍﻣﺮ ﻣﻨﻔﻲ‬
‫ﻥ ‪ +‬ﺑﻦ ﻣﻀﺎﺭﻉ ‪,""}+‬ﻳﻢ‪,‬ﻳﺪ{‬
‫ﺩﻳﮕﺮ ﺍﻓﻌﺎﻝ ﻣﻨﻔﻲ‬
‫ﻥ ‪} +‬ﻣﺎﺿﻲ ﺳﺎﺩﻩ‪ ,‬ﻣﺎﺿﻲ ﺍﺳﺘﻤﺮﺍﺭﻱ‪ ,‬ﻣﺎﺿﻲ ﻧﻘﻠﻲ‪,‬‬
‫ﻣﺎﺿﻲ ﺑﻌﻴﺪ‪ ,‬ﻣﺎﺿﻲ ﺍﻟﺘﺰﺍﻣﻲ‪ ,‬ﻣﻀﺎﺭﻉ ﺍﺧﺒﺎﺭﻱ{‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‬
‫ﺩﺭ ﺍﺑﺘﺪﺍ ﺑﻪ ﺑﺮﺭﺳﻲ ﻃﺮﻳﻘﻪ ﺳﺎﺧﺖ ﺍﻓﻌﺎﻝ ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﻣﻲﭘﺮﺩﺍﺯﻳﻢ‪ .‬ﺩﺭ‬
‫ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﺩﻭ ﺭﻳﺸﻪ ﺑﺮﺍﻱ ﻫﺮ ﻋﻤﻞ ﻭﺟﻮﺩ ﺩﺍﺭﺩ‪ .‬ﺑﻦ ﻣﺎﺿﻲ ﻭ ﺑﻦ ﻣﻀﺎﺭﻉ‪.‬‬
‫ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﺑﺮﺍﻱ ﻓﻌﻞ ﺩﻳﺪﻥ ﺩﻭ ﺑﻦ )ﺩﻳﺪ ‪:‬ﺑﻦ ﻣﺎﺿﻲ( ﻭ )ﺑﻴﻦ ‪ :‬ﺑﻦ‬
‫ﻣﻀﺎﺭﻉ( ﻣﻲﺷﻨﺎﺳﻴﻢ‪.‬‬
‫ﺗﻤﺎﻡ ﺍﻓﻌﺎﻝ ﺳﺎﺩﻩ ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﺑﺎ ﺍﻓﺰﻭﺩﻥ ﭘﻴﺶﻭﻧﺪﻫﺎ ﻭ ﭘﺲﻭﻧﺪﻫﺎ ﺑﻪ‬
‫ﻓﻌﻞ ﺑﻪ ﻭﺟﻮﺩ ﻣﻲﺁﻳﻨﺪ‪ .‬ﺩﺭ ﺟﺪﻭﻝ ‪ ۲‬ﺗﻤﺎﻡ ﻗﻮﺍﻧﻴﻦ ﺳﺎﺧﺖ ﺍﻓﻌﺎﻝ ﻋﻤﺪﻩ‬
‫ﻓﺎﺭﺳﻲ ﺭﺍ ﺁﻭﺭﺩﻩﺍﻳﻢ‪.‬‬
‫ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﺗﻐﻴﻴﺮﺍﺕ ﺩﺭ ﺑﻦ ﻣﺎﺿﻲ ﻭ ﻣﻀﺎﺭﻉ ﺗﻨﻬﺎ ﺑﻪ ﻣﻮﺍﺭﺩﻱ ﮐﻪ ﺑﻦ‬
‫ﻣﺎﺿﻲ ﻳﺎ ﻣﻀﺎﺭﻉ ﺑﺎ ﻳﮏ ﺣﺮﻑ ﺻﺪﺍ ﺩﺍﺭ ﺁﻏﺎﺯ ﻳﺎ ﺧﺎﺗﻤﻪ ﻣﻲ ﻳﺎﺑﺪ ﺩﺭ ﻣﻮﺍﺭﺩ‬
‫ﺧﺎﺻﻲ ﺻﻮﺭﺕ ﻣﻲ ﮔﻴﺮﺩ‪ .‬ﮐﻪ ﺍﻳﻢ ﻣﻮﺍﺭﺩ ﻫﻢ ﺩﺭ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺩﺭ ﻧﻈﺮ ﮔﺮﻓﺘﻪ‬
‫ﺷﺪﻩﺍﻧﺪ‪.‬‬
‫ﺩﺭ ﺟﺪﻭﻝ ﺷﻤﺎﺭﻩ ‪ ۳‬ﻭ ‪ ۴‬ﺗﻮﺍﺑﻊ ﻭ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﮐﻪ ﺑﺮﺍﻱ ﺭﻳﺸﻪ ﻳﺎﺑﻲ‬
‫ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﻣﻌﺮﻓﻲ ﺷﺪﻩ ﺁﻣﺪﻩ ﺍﺳﺖ‪ .‬ﺍﺻﻮﻝ ﮐﻠﻲ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺑﺮ ﺍﺳﺎﺱ‬
‫ﺟﺪﻭﻝ ‪ ۲‬ﺑﺪﺳﺖ ﺁﻣﺪﻩ ﺍﺳﺖ‪.‬‬
‫‪٢‐٣‬‬
‫ﺟﺪﻭﻝ‪ ۲‬ﻟﻴﺴﺖ ﺍﻓﻌﺎﻝ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‪ ٢‬ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﻫﺮﭼﻨﺪ ﺩﺍﺭﺍﻱ ﻧﺘﺎﻳﺞ ﺧﻮﺑﻲ ﺑﻮﺩﻩ ﺍﺳﺖ‪ ،‬ﺍﻣﺎ ﺩﺭ ﺟﺪﻭﻝ ‪۵‬‬
‫ﻣﺜﺎﻝﻫﺎﻳﻲ ﺭﺍ ﻣﺸﺎﻫﺪﻩ ﻣﻲﮐﻨﻴﺪ ﮐﻪ ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ ﻧﻤﻲﺗﻮﺍﻧﺪ ﺑﻪ ﺟﻮﺍﺏ‬
‫ﺩﺭﺳﺖ ﺩﺳﺖ ﻳﺎﺑﺪ‪.‬‬
‫ﺩﻟﻴﻞ ﺍﻳﻦ ﻣﺴﺎﻟﻪ ﺩﺭ ﻗﺒﻞ ﻫﻢ ﺷﺮﺡ ﺩﺍﺩﻩ ﺷﺪ‪ .‬ﺍﺯ ﺁﻧﺠﺎ ﮐﻪ ﮐﻠﻤﺎﺕ "ﺭﻭﻡ" ﻭ‬
‫"ﻣﺎﻧﻨﺪ" ﺩﺭ ﻓﺮﻫﻨﮓ ﻟﻐﺖ ﻭﺟﻮﺩ ﺩﺍﺭﺩ ﺍﻳﻦ ﺍﺷﺘﺒﺎﻫﺎﺕ ﺭﺥ ﻣﻲ ﺩﻫﺪ‪ .‬ﺑﻪ ﻫﻤﻴﻦ‬
‫ﻣﻨﻈﻮﺭ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‪ ۲‬ﻫﻢ ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﻧﻮﺷﺘﻪ ﺷﺪﻩ ﺍﺳﺖ ‪.‬‬
‫ﺩﺭ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‪ ۲‬ﺩﺭ ﻫﺮ ﻗﺴﻤﺖ ﻣﺸﺨﺺ ﻣﻲ ﮐﻨﻴﻢ ﮐﻪ ﭼﻪ ﻧﻮﻉ ﮐﻠﻤﻪ‬
‫ﺍﻱ ) ﺑﻦ ﻣﻀﺎﺭﻉ ﻳﺎ ﺑﻦ ﻣﺎﺿﻲ( ﻣﻮﺭﺩ ﻗﺒﻮﻝ ﺍﺳﺖ‪ .‬ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺗﻮﺍﻧﺴﺘﻪ‬
‫ﺍﺳﺖ ﺍﺷﺘﺒﺎﻫﺎﺕ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺭﺍ ﺗﺎ ﺣﺪﻭﺩﻱ ﺑﺮﻃﺮﻑ ﮐﻨﺪ ﮐﻪ ﺩﺭ ﺑﺨﺶ‬
‫ﻧﺘﺎﻳﺞ ﺷﺮﺡ ﺩﺍﺩﻩﺍﻳﻢ‪.‬‬
‫ﺟﺪﻭﻝ ‪ ٣‬ﻟﻴﺴﺖ ﺗﻮﺍﺑﻊ ﻣﻮﺭﺩ ﺍﺳﺘﻔﺎﺩﻩ ﺩﺭ ﮐﺮﺍﻭﺗﺰ‪ ۲‬ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‬
‫ﮐﻠﻤﻪ ‪ w‬ﺭﺍ ﺩﺭ ﻓﺮﻫﻨﮓ ﻟﻐﺖ ﺟﺴﺘﺠﻮ ﻣﻲﮐﻨﺪ‪.‬‬
‫)‪Find (w‬‬
‫ﺍﮔﺮ ﭘﻴﺸﻮﻧﺪ ‪ pfr‬ﺩﺭ ﮐﻠﻤﻪ ‪ W‬ﻭﺟﻮﺩ ﺩﺍﺷﺘﻪ ﺑﺎﺷﺪ‬
‫ﺩﺭﺳﺖ ﺧﻮﺍﻫﺪ ﺑﻮﺩ‪.‬‬
‫)‪Prefix (W, prf‬‬
‫ﺍﮔﺮ ﭘﺴﻮﻧﺪ ‪ pof‬ﺩﺭ ﮐﻠﻤﻪ ‪ W‬ﻭﺟﻮﺩ ﺩﺍﺷﺘﻪ ﺑﺎﺷﺪ‬
‫ﺩﺭﺳﺖ ﺧﻮﺍﻫﺪ ﺑﻮﺩ‪.‬‬
‫) ‪Postfix (W, pof‬‬
‫ﭘﻴﺸﻮﻧﺪ ‪ prf‬ﺭﺍ ﺍﺯ ﺍﺑﺘﺪﺍﻱ ﮐﻠﻤﻪ ‪ w‬ﺣﺬﻑ ﻣﻲﮐﻨﺪ‪.‬‬
‫)‪RePrefix (W, prf‬‬
‫ﭘﺴﻮﻧﺪ ‪ prf‬ﺭﺍ ﺍﺯ ﺍﻧﺘﻬﺎﻱ ﮐﻠﻤﻪ ‪ w‬ﺣﺬﻑ ﻣﻲﮐﻨﺪ‪.‬‬
‫) ‪RePostfix (W, pof‬‬
‫ ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‬۲‫ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‬۴ ‫ﺟﺪﻭﻝ‬
Word: The word for stemming.
Result: result of stemming.
Æ
//if the word is in dictionary don’t need stemming
If(PostFix(Word,{ “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫{)} ” ﻧﺪ‬
W1 = Word;
RePostFix(Word, { “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫;)} ” ﻧﺪ‬
If find(Word)
If(find(Word){
Result += Word + “+”;
Result = Word;
//This part if for werb begin with “‫”ﻣﻲ‬
Result += { “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫;} ” ﻧﺪ‬
‫ﻣﻀﺎرع اﺧﺒﺎري و ﻣﺎﺿﻲ اﻟﺘﺰاﻣﻲ‬//
Return;}}
If (PreFix (Word,”‫{)”ﻣﻲ‬
Word = W1;
Reprefix(Word,”‫;)”ﻣﻲ‬
‫ﻣﺎﺿﻲ ﺳﺎدﻩ‬//
Result +=”‫;”ﻣﻲ‬
If(PostFix(Word,{ “‫”م‬, “‫” ي‬, “‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫{)} ” ﻧﺪ‬
If find(Word){
RePostFix(Word, { “‫”م‬, “‫” ي‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫;)} ” ﻧﺪ‬
If(find(Word){
Result += Word;
Return;
Result += Word + “+”;
}
If(PostFix(Word,{ “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫{)} ” ﻧﺪ‬
Result += { “‫”م‬, “‫” ي‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫;} ” ﻧﺪ‬
RePostFix(Word, { “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫;)} ” ﻧﺪ‬
Return;}
If(find(Word){
Word = W1;
Result += Word + “+”;
‫ﻣﺎﺿﻲ ﻧﻘﻠﻲ‬//
Result += { “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫;} ” ﻧﺪ‬
If(PostFix(Word,{ “‫”ﻩ ام‬, “‫”ﻩ ا ي‬, “‫ ”ﻩ اﺳﺖ‬,”‫ ”ﻩ اﻳﻢ‬,“‫ ” ﻩ اﻳﺪ‬,
Return;}}
RePostFix(Word, { “‫”ﻩ ام‬, “‫”ﻩ ا ي‬, “‫ ”ﻩ اﺳﺖ‬,”‫ ”ﻩ اﻳﻢ‬,“‫ ” ﻩ اﻳﺪ‬,
“‫{)}”ﻩ اﻧﺪ‬
“‫;)}”ﻩ اﻧﺪ‬
If(find(Word){
Word = W1;
Result += Word + “+”;
‫اﻣﺮ و ﻣﺎﺿﻲ ﺳﺎدﻩ‬//
Result += { “‫”ﻩ ام‬, “‫”ﻩ ا ي‬, “‫ ”ﻩ اﺳﺖ‬,”‫ ”ﻩ اﻳﻢ‬,“‫ ” ﻩ اﻳﺪ‬, “‫;}”ﻩ اﻧﺪ‬
If (PreFix (Word,”‫{)”ب‬
Reprefix(Word,”‫;)”ب‬
Return;}
Result +=”‫;”ب‬
Word = W1;
If find(Word){
‫ﻣﺎﺿﻲ ﺑﻌﻴﺪ‬//
Result += Word;
If(PostFix(Word,{ “‫”ﻩ ﺑﻮدم‬, “‫”ﻩ ﺑﻮدي‬, “‫ ”ﻩ ﺑﻮد‬,”‫ ”ﻩ ﺑﻮدﻳﻢ‬,“‫ ” ﻩ ﺑﻮدﻳﺪ‬, “‫{)}”ﻩ ﺑﻮدﻧﺪ‬
Return;
RePostFix(Word, { “‫”ﻩ ﺑﻮدم‬, “‫”ﻩ ﺑﻮدي‬, “‫ ”ﻩ ﺑﻮد‬,”‫ ”ﻩ ﺑﻮدﻳﻢ‬,
}
Result += Word + “+”;
RePostFix(Word, { “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫;)} ” ﻧﺪ‬
If(find(Word){
Result += Word + “+”;
“‫ ” ﻩ ﺑﻮدﻳﺪ‬, “‫;)}”ﻩ ﺑﻮدﻧﺪ‬
If(find(Word){
If(PostFix(Word,{ “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫{)} ” ﻧﺪ‬
Result += { “‫”ﻩ ﺑﻮدم‬, “‫”ﻩ ﺑﻮدي‬, “‫ ”ﻩ ﺑﻮد‬,”‫ ”ﻩ ﺑﻮدﻳﻢ‬,“‫ ” ﻩ ﺑﻮدﻳﺪ‬,
“‫;}”ﻩ ﺑﻮدﻧﺪ‬
Return;}
Result += { “‫”م‬, “‫” ي‬, “‫” د‬,”‫ ”ﻳﻢ‬,”‫ ”ﻳﺪ‬,“‫;} ” ﻧﺪ‬
Word = W1;
Return;}}
‫ﻣﺎﺿﻲ اﻟﺘﺰاﻣﻲ‬//
‫اﻣﺮ و ﻣﺎﺿﻲ ﺳﺎدﻩ ﻣﻨﻔﻲ‬//
If(PostFix(Word,{ “‫”ﻩ ﺑﺎﺷﻢ‬, “‫”ﻩ ﺑﺎﺷﻲ‬, “‫ ”ﻩ ﺑﺎﺷﺪ‬,”‫ ”ﻩ ﺑﺎﺷﻴﻢ‬,
“‫ ” ﻩ ﺑﺎﺷﻴﺪ‬, “‫{)}”ﻩ ﺑﺎﺷﻨﺪ‬
If (PreFix (Word,”‫{)”ن‬
RePostFix(Word, { “‫”ﻩ ﺑﺎﺷﻢ‬, “‫”ﻩ ﺑﺎﺷﻲ‬, “‫ ”ﻩ ﺑﺎﺷﺪ‬,”‫ ”ﻩ ﺑﺎﺷﻴﻢ‬,
“‫ ” ﻩ ﺑﺎﺷﻴﺪ‬, “‫;)}”ﻩ ﺑﺎﺷﻨﺪ‬
If(find(Word){
Reprefix(Word,”‫;)”ن‬
Result += Word + “+”;
Result +=”‫;”ن‬
If find(Word){
Result += Word;
Return;}
Æ
Result += { “‫”ﻩ ﺑﺎﺷﻢ‬, “‫”ﻩ ﺑﺎﺷﻲ‬, “‫ ”ﻩ ﺑﺎﺷﺪ‬,”‫ ”ﻩ ﺑﺎﺷﻴﻢ‬,“‫ ” ﻩ ﺑﺎﺷﻴﺪ‬, “‫;}”ﻩ ﺑﺎﺷﻨﺪ‬
Return;}
Word = W1;
If (prefix(Word,”‫)”ن‬
{//Remove the “‫ ”ن‬from prefix and check all part for it according the table 1}
‫ﺟﺪﻭﻝ‪ ۵‬ﺑﺮﺧﻲ ﺍﺯ ﻣﺜﺎﻝﻫﺎﻳﻲ ﮐﻪ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﭘﺎﺳﺦ ﻧﺎﺩﺭﺳﺖ ﺑﻪ ﺁﻧﻬﺎ ﺩﺍﺩﻩ ﺍﺳﺖ‪.‬‬
‫ﮐﻠﻤﻪ‬
‫ﺭﻳﺸﻪ ﻳﺎﻓﺖ ﺷﺪﻩ‬
‫ﺭﻳﺸﻪ ﺩﺭﺳﺖ‬
‫ﻣﻲﺭﻭﻡ‬
‫ﻣﻲ ‪ +‬ﺭﻭﻡ‬
‫ﻣﻲ ‪ +‬ﺭﻭ ‪ +‬ﻡ‬
‫ﻣﻲﻣﺎﻧﻨﺪ‬
‫ﻣﻲ ‪ +‬ﻣﺎﻧﻨﺪ‬
‫ﻣﻲ ‪ +‬ﻣﺎﻥ‪ +‬ﻧﺪ‬
‫‪ ‐٤‬ﻧﺘﺎﻳﺞ ﺑﺪﺳﺖ ﺁﻣﺪﻩ‬
‫ﺑﺮﺍﻱ ﺁﺯﻣﻮﺩﻥ ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎ ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﺍﺯ ‪ ۱۰۰۰‬ﺟﻤﻠﻪ ﻓﺎﺭﺳﻲ‬
‫ﺍﺳﺘﻔﺎﺩﻩ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺳﭙﺲ ﺗﻤﺎﻡ ﺍﻓﻌﺎﻝ ﺗﻮﺳﻂ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﺑﺎﻻ ﺭﻳﺸﻪﻳﺎﺑﻲ‬
‫ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺩﺭ ﻧﻬﺎﻳﺖ ﻧﺘﺎﻳﺞ ﺟﺪﻭﻝ ‪ ۶‬ﺑﺪﺳﺖ ﺁﻣﺪﻩ ﺍﺳﺖ‪ .‬ﻫﻤﺎﻥ ﻃﻮﺭ ﮐﻪ‬
‫ﺩﻳﺪﻩ ﻣﻲﺷﻮﺩ‪ ،‬ﻫﺮ ﺩﻭ ﺍﻟﮕﻮﺭﻳﺘﻢ ﺩﺍﺭﺍﻱ ﻧﺘﺎﻳﺞ ﺧﻮﺑﻲ ﻫﺴﺘﻨﺪ‪ .‬ﺍﺯ ﺗﻌﺪﺍﺩ ‪۲۲۳‬‬
‫ﮐﻠﻤﻪﺍﻱ ﮐﻪ ﺑﺎ ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ ﻫﺎ ﺗﺴﺖ ﺷﺪﻩ ﺍﺳﺖ‪ ،‬ﺗﻨﻬﺎ ‪ ۱۳‬ﻣﻮﺭﺩ ﺩﺭ‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺍﺷﺘﺒﺎﻩ ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﺷﺪﻩ‪ .‬ﺍﻳﻦ ﺭﻗﻢ ﺩﺭ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‪ ۲‬ﺑﻪ‬
‫‪ ۴‬ﻣﻮﺭﺩ ﺗﻘﻠﻴﻞ ﻳﺎﻓﺘﻪ ﺍﺳﺖ‪ .‬ﺑﻪ ﻋﺒﺎﺭﺕ ﺩﻳﮕﺮ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺑﻬﻴﻨﻪ ﺳﺎﺯﻱ‬
‫ﺍﻧﺠﺎﻡ ﺷﺪﻩ ﻣﻲ ﺗﻮﺍﻥ ﺣﺪﻭﺩ ‪ ۶۰‬ﺩﺭﺻﺪ ﺍﺷﺘﺒﺎﻫﺎﺕ ﺭﺍ ﺍﺻﻼﺡ ﮐﺮﺩﻩ ﺍﺳﺖ‪.‬‬
‫ﻧﮑﺘﻪ ﻗﺎﺑﻞ ﺗﻮﺟﻪ ﺍﻳﻨﮑﻪ ﺩﺭ ﺗﻨﻬﺎ ﺩﺭ ﻣﻮﺭﺩ ﺍﻓﻌﺎﻝ ﻣﺮﮐﺐ ﺍﺷﺘﺒﺎﻩ ﺭﺥ ﺩﺍﺩﻩ‬
‫ﺍﺳﺖ‪ ،‬ﮐﻪ ﺑﺮﺍﻱ ﺁﻧﻬﺎ ﻗﺎﻧﻮﻧﻲ ﻭﺟﻮﺩ ﻧﺪﺍﺷﺘﻪ ﺍﺳﺖ‪.‬‬
‫ﻫﻤﺎﻥ ﻃﻮﺭ ﮐﻪ ﻣﻼﺣﻈﻪ ﻣﻲ ﺷﻮﺩ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎ ﻫﺮ ﺩﻭ ﺩﺍﺭﺍﻱ ﻧﺘﺎﻳﺞ ﺧﻮﺑﻲ‬
‫ﺑﻮﺩﻩﺍﻧﺪ ﮐﻪ ﺍﻟﺒﺘﻪ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ‪ ۲‬ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﺑﻬﺘﺮ ﻋﻤﻞ ﮐﺮﺩﻩ‬
‫ﺍﺳﺖ‪.‬‬
‫ﺑﺮﺍﻱ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ ﮐﻪ ﺩﺍﺭﺍﻱ ﺳﺎﺧﺘﺎﺭ ﺭﻳﺨﺘﻲ ﻣﻨﻈﻤﻲ ﺍﺳﺖ ﺍﻟﮕﻮﺭﻳﺘﻢ ﻫﺎﻱ‬
‫ﺭﻳﺸﻪﻳﺎﺑﻲ ﺯﻳﺎﺩﻱ ﻣﻌﺮﻓﻲ ﺷﺪﻩ ﺍﺳﺖ‪ ،‬ﺍﻣﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻧﻘﺶ ﮐﻠﻤﺎﺕ ﻫﻤﺎﻥ‬
‫ﮔﻮﻧﻪ ﮐﻪ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺭﺍ ﺑﻬﻴﻨﻪ ﮐﺮﺩﻩ ﺍﺳﺖ‪ .‬ﺗﺎﺛﻴﺮ ﺧﻮﺑﻲ ﻫﻢ ﺑﺮ ﺭﻭﻱ‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﻫﺎﻱ ﺭﻳﺨﺖ ﺷﻨﺎﺳﻲ ﻓﺎﺭﺳﻲ ﺩﺍﺷﺖ‪.‬‬
‫ﺑﺎ ﺍﻳﻦ ﺣﺎﻝ ﺍﻳﻦ ﺭﻭﺵ ﺭﺍ ﺗﻨﻬﺎ ﺑﺮﺍﻱ ﺍﻓﻌﺎﻝ ﺑﻪ ﮐﺎﺭ ﺑﺮﺩﻩﺍﻳﻢ ﮐﻪ ﺑﺮﺍﻱ ﺩﻳﮕﺮ‬
‫ﮔﺮﻭﻩﻫﺎﻱ ﺍﺳﺎﻣﻲ ﻗﺎﺑﻞ ﺗﻌﻤﻴﻢ ﺍﺳﺖ‪.‬‬
‫‪ ‐٦‬ﻣﺮﺍﺟﻊ‬
‫‪[1] R. Krovetz, 1993: “Viewing morphology as an inference‬‬
‫‪process”, in R. Korfhage et al., Proc. 16th ACM SIGIR‬‬
‫‪Conference, Pittsburgh, June 27-July 1, 1993; pp. 191-202.‬‬
‫‪[2] J. P. Callan, M. Connell, A. Du. “Automatic discovery of‬‬
‫‪language models for text databases”, ACM SIGMOD‬‬
‫‪International Conference on Management of Data, pages 479‬‬‫‪490, 1999.‬‬
‫‪[3] K. Aas and L. Eikvil, “Text Categorisation: A Survey”,‬‬
‫‪http://citeseer.nj.nec.com/ , aas99text.html, 1999.‬‬
‫‪ ‐٥‬ﻧﺘﻴﺠﻪﮔﻴﺮﻱ‬
‫‪[4] M. F. Porter. “An algorithm for suffix stripping”, Program,‬‬
‫‪14(3):130t137, 1980.‬‬
‫ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺭﻭﻳﮑﺮﺩﻫﺎﻱ ﻣﺨﺘﻠﻒ ﺭﺍ ﺑﻪ ﻣﺴﺎﻟﻪ ﺭﻳﺸﻪﻳﺎﺑﻲ ﻣﻮﺭﺩ ﺑﺮﺭﺳﻲ ﻗﺮﺍﺭ‬
‫ﺩﺍﺩﻳﻢ‪ .‬ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﺩﺭ ﺑﺴﻴﺎﺭﻱ ﺍﺯ ﺳﺎﻣﺎﻧﻪ ﻫﺎﻱ ﭘﺮﺩﺍﺯﺵ ﺯﺑﺎﻥ ﻃﺒﻴﻌﻲ ﮐﺎﺭﺑﺮﺩ‬
‫ﺩﺍﺭﺩ‪ .‬ﺍﺯ ﻣﻴﺎﻥ ﺩﻭ ﺭﻭﻳﮑﺮﺩ ﺑﻪ ﺣﻞ ﻣﺴﺎﻟﻪ ﺭﻭﻳﮑﺮﺩ ﺭﻳﺨﺖ ﺷﻨﺎﺳﻲ ﺭﺍ ﺑﺮﺭﺳﻲ‬
‫ﮐﺮﺩﻳﻢ ﻭ ﻳﮑﻲ ﺍﺯ ﺍﻳﻦ ﺭﻭﺵﻫﺎ ﮐﻪ ﺍﻟﮕﻮﺭﻳﺘﻢ ﮐﺮﺍﻭﺗﺰ ﺑﻮﺩ ﻣﻌﺮﻓﻲ ﺷﺪ‪ .‬ﺑﺎ‬
‫ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺍﻳﻦ ﺍﻟﮕﻮﺭﻳﺘﻢ ﻧﺴﺨﻪ ﺟﺪﻳﺪﻱ ﺍﺯ ﺭﻭﺵﻫﺎﻱ ﺭﻳﺸﻪﻳﺎﺑﻲ ﺭﺍ ﻣﻌﺮﻓﻲ‬
‫ﮐﺮﺩﻳﻢ ﮐﻪ ﺍﺯ ﻧﻘﺶ ﮐﻠﻤﺎﺕ ﺑﺮﺍﻱ ﺭﻳﺸﻪﻳﺎﺑﻲ ﺍﺳﺘﻔﺎﺩﻩ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫‪[5] E.M Voorhees, “using wordnet for text retrieval”,‬‬
‫‪WordNet an electronic lexical Database, Page 285-303, MIT‬‬
‫‪Press.‬‬
‫‪[6] Kazem Taghva, Russell Beckley, Mohammad Sadeh, “A‬‬
‫‪Stemming Algorithm for the Farsi Language”. ITCC (1): 158‬‬‫‪162, 2005.‬‬
‫ﺟﺪﻭﻝ ‪ ٦‬ﻧﺘﺎﻳﺞ ﺑﺪﺳﺖ ﺁﻣﺪﻩ ﺑﺮﺍﻱ ﺍﻟﮕﻮﺭﻳﺘﻢ ﻫﺎﻱ ﺭﻳﺸﻪ ﻳﺎﺑﻲ ﺩﺭ ﺯﺑﺎﻥ ﻓﺎﺭﺳﻲ‬
‫ﺩﺭﺻﺪ ﺩﺭﺳﺘﻲ‬
‫ﻧﺘﺎﻳﺞ ﻧﺎﺩﺭﺳﺖ‬
‫ﻧﺘﺎﻳﺞ ﺩﺭﺳﺖ‬
‫ﺗﻌﺪﺍﺩ ﮐﻞ ﮐﻠﻤﺎﺕ‬
‫ﺍﻟﮕﻮﺭﻳﺘﻢ ﻣﻮﺭﺩ ﺍﺳﺘﻔﺎﺩﻩ‬
‫‪٩۴٫١٧‬‬
‫‪١٣‬‬
‫‪٢١٠‬‬
‫‪٢٢٣‬‬
‫ﮐﺮاوﺗﺰ‬
‫‪٩٨٫٢٠‬‬
‫‪۴‬‬
‫‪٢١٩‬‬
‫‪٢٢٣‬‬
‫ﮐﺮاوﺗﺰ‪٢‬‬