Semi-supervised learning for question classification in CQA

Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of label...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Yiyang [verfasserIn] Su, Lei [verfasserIn] Chen, Jun [verfasserIn] Yuan, Liwei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Community Q&A Semi-supervised learning Ensemble learning Question classification

Übergeordnetes Werk:	Enthalten in: Natural computing - Dordrecht : Springer Science + Business Media B.V., 2002, 16(2016), 4 vom: 05. Mai, Seite 567-577
Übergeordnetes Werk:	volume:16 ; year:2016 ; number:4 ; day:05 ; month:05 ; pages:567-577

Links:	Volltext

DOI / URN:	10.1007/s11047-016-9554-5

Katalog-ID:	SPR016347730

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520			\|a Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy.
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allfields	10.1007/s11047-016-9554-5 doi (DE-627)SPR016347730 (SPR)s11047-016-9554-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.28 bkl 54.72 bkl Li, Yiyang verfasserin aut Semi-supervised learning for question classification in CQA 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy. Community Q&A (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Question classification (dpeaa)DE-He213 Su, Lei verfasserin aut Chen, Jun verfasserin aut Yuan, Liwei verfasserin aut Enthalten in Natural computing Dordrecht : Springer Science + Business Media B.V., 2002 16(2016), 4 vom: 05. Mai, Seite 567-577 (DE-627)340872314 (DE-600)2065639-7 1572-9796 nnns volume:16 year:2016 number:4 day:05 month:05 pages:567-577 https://dx.doi.org/10.1007/s11047-016-9554-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.28 ASE 54.72 ASE AR 16 2016 4 05 05 567-577
spelling	10.1007/s11047-016-9554-5 doi (DE-627)SPR016347730 (SPR)s11047-016-9554-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.28 bkl 54.72 bkl Li, Yiyang verfasserin aut Semi-supervised learning for question classification in CQA 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy. Community Q&A (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Question classification (dpeaa)DE-He213 Su, Lei verfasserin aut Chen, Jun verfasserin aut Yuan, Liwei verfasserin aut Enthalten in Natural computing Dordrecht : Springer Science + Business Media B.V., 2002 16(2016), 4 vom: 05. Mai, Seite 567-577 (DE-627)340872314 (DE-600)2065639-7 1572-9796 nnns volume:16 year:2016 number:4 day:05 month:05 pages:567-577 https://dx.doi.org/10.1007/s11047-016-9554-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.28 ASE 54.72 ASE AR 16 2016 4 05 05 567-577
allfields_unstemmed	10.1007/s11047-016-9554-5 doi (DE-627)SPR016347730 (SPR)s11047-016-9554-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.28 bkl 54.72 bkl Li, Yiyang verfasserin aut Semi-supervised learning for question classification in CQA 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy. Community Q&A (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Question classification (dpeaa)DE-He213 Su, Lei verfasserin aut Chen, Jun verfasserin aut Yuan, Liwei verfasserin aut Enthalten in Natural computing Dordrecht : Springer Science + Business Media B.V., 2002 16(2016), 4 vom: 05. Mai, Seite 567-577 (DE-627)340872314 (DE-600)2065639-7 1572-9796 nnns volume:16 year:2016 number:4 day:05 month:05 pages:567-577 https://dx.doi.org/10.1007/s11047-016-9554-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.28 ASE 54.72 ASE AR 16 2016 4 05 05 567-577
allfieldsGer	10.1007/s11047-016-9554-5 doi (DE-627)SPR016347730 (SPR)s11047-016-9554-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.28 bkl 54.72 bkl Li, Yiyang verfasserin aut Semi-supervised learning for question classification in CQA 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy. Community Q&A (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Question classification (dpeaa)DE-He213 Su, Lei verfasserin aut Chen, Jun verfasserin aut Yuan, Liwei verfasserin aut Enthalten in Natural computing Dordrecht : Springer Science + Business Media B.V., 2002 16(2016), 4 vom: 05. Mai, Seite 567-577 (DE-627)340872314 (DE-600)2065639-7 1572-9796 nnns volume:16 year:2016 number:4 day:05 month:05 pages:567-577 https://dx.doi.org/10.1007/s11047-016-9554-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.28 ASE 54.72 ASE AR 16 2016 4 05 05 567-577
allfieldsSound	10.1007/s11047-016-9554-5 doi (DE-627)SPR016347730 (SPR)s11047-016-9554-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.28 bkl 54.72 bkl Li, Yiyang verfasserin aut Semi-supervised learning for question classification in CQA 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy. Community Q&A (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Question classification (dpeaa)DE-He213 Su, Lei verfasserin aut Chen, Jun verfasserin aut Yuan, Liwei verfasserin aut Enthalten in Natural computing Dordrecht : Springer Science + Business Media B.V., 2002 16(2016), 4 vom: 05. Mai, Seite 567-577 (DE-627)340872314 (DE-600)2065639-7 1572-9796 nnns volume:16 year:2016 number:4 day:05 month:05 pages:567-577 https://dx.doi.org/10.1007/s11047-016-9554-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.28 ASE 54.72 ASE AR 16 2016 4 05 05 567-577
language	English
source	Enthalten in Natural computing 16(2016), 4 vom: 05. Mai, Seite 567-577 volume:16 year:2016 number:4 day:05 month:05 pages:567-577
sourceStr	Enthalten in Natural computing 16(2016), 4 vom: 05. Mai, Seite 567-577 volume:16 year:2016 number:4 day:05 month:05 pages:567-577
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Community Q&A Semi-supervised learning Ensemble learning Question classification
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container_title	Natural computing
authorswithroles_txt_mv	Li, Yiyang @@aut@@ Su, Lei @@aut@@ Chen, Jun @@aut@@ Yuan, Liwei @@aut@@
publishDateDaySort_date	2016-05-05T00:00:00Z
hierarchy_top_id	340872314
dewey-sort	14
id	SPR016347730
language_de	englisch
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And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. 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author	Li, Yiyang
spellingShingle	Li, Yiyang ddc 004 bkl 54.28 bkl 54.72 misc Community Q&A misc Semi-supervised learning misc Ensemble learning misc Question classification Semi-supervised learning for question classification in CQA
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topic_title	004 ASE 54.28 bkl 54.72 bkl Semi-supervised learning for question classification in CQA Community Q&A (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Ensemble learning (dpeaa)DE-He213 Question classification (dpeaa)DE-He213
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title	Semi-supervised learning for question classification in CQA
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title_full	Semi-supervised learning for question classification in CQA
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title_sort	semi-supervised learning for question classification in cqa
title_auth	Semi-supervised learning for question classification in CQA
abstract	Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy.
abstractGer	Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy.
abstract_unstemmed	Abstract In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy.
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title_short	Semi-supervised learning for question classification in CQA
url	https://dx.doi.org/10.1007/s11047-016-9554-5
remote_bool	true
author2	Su, Lei Chen, Jun Yuan, Liwei
author2Str	Su, Lei Chen, Jun Yuan, Liwei
ppnlink	340872314
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11047-016-9554-5
up_date	2024-07-03T22:32:46.642Z
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