Short text keyphrase extraction with hypergraphs
Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the wh...
Ausführliche Beschreibung
Autor*in: |
Bellaachia, Abdelghani [verfasserIn] Al-Dhelaan, Mohammed [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2014 |
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Übergeordnetes Werk: |
Enthalten in: Progress in artificial intelligence - Berlin : Springer, 2012, 3(2014), 2 vom: 20. Aug., Seite 73-87 |
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Übergeordnetes Werk: |
volume:3 ; year:2014 ; number:2 ; day:20 ; month:08 ; pages:73-87 |
Links: |
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DOI / URN: |
10.1007/s13748-014-0058-1 |
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Katalog-ID: |
SPR032252358 |
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520 | |a Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. | ||
650 | 4 | |a Keyphrase extraction |7 (dpeaa)DE-He213 | |
650 | 4 | |a Hypergraph ranking |7 (dpeaa)DE-He213 | |
650 | 4 | |a Text hypergraph |7 (dpeaa)DE-He213 | |
650 | 4 | |a Hypergraph random walks |7 (dpeaa)DE-He213 | |
700 | 1 | |a Al-Dhelaan, Mohammed |e verfasserin |4 aut | |
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10.1007/s13748-014-0058-1 doi (DE-627)SPR032252358 (SPR)s13748-014-0058-1-e DE-627 ger DE-627 rakwb eng 004 600 ASE Bellaachia, Abdelghani verfasserin aut Short text keyphrase extraction with hypergraphs 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. Keyphrase extraction (dpeaa)DE-He213 Hypergraph ranking (dpeaa)DE-He213 Text hypergraph (dpeaa)DE-He213 Hypergraph random walks (dpeaa)DE-He213 Al-Dhelaan, Mohammed verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 3(2014), 2 vom: 20. Aug., Seite 73-87 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:3 year:2014 number:2 day:20 month:08 pages:73-87 https://dx.doi.org/10.1007/s13748-014-0058-1 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_65 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_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_2232 GBV_ILN_2244 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 AR 3 2014 2 20 08 73-87 |
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10.1007/s13748-014-0058-1 doi (DE-627)SPR032252358 (SPR)s13748-014-0058-1-e DE-627 ger DE-627 rakwb eng 004 600 ASE Bellaachia, Abdelghani verfasserin aut Short text keyphrase extraction with hypergraphs 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. Keyphrase extraction (dpeaa)DE-He213 Hypergraph ranking (dpeaa)DE-He213 Text hypergraph (dpeaa)DE-He213 Hypergraph random walks (dpeaa)DE-He213 Al-Dhelaan, Mohammed verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 3(2014), 2 vom: 20. Aug., Seite 73-87 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:3 year:2014 number:2 day:20 month:08 pages:73-87 https://dx.doi.org/10.1007/s13748-014-0058-1 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_65 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_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_2232 GBV_ILN_2244 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 AR 3 2014 2 20 08 73-87 |
allfields_unstemmed |
10.1007/s13748-014-0058-1 doi (DE-627)SPR032252358 (SPR)s13748-014-0058-1-e DE-627 ger DE-627 rakwb eng 004 600 ASE Bellaachia, Abdelghani verfasserin aut Short text keyphrase extraction with hypergraphs 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. Keyphrase extraction (dpeaa)DE-He213 Hypergraph ranking (dpeaa)DE-He213 Text hypergraph (dpeaa)DE-He213 Hypergraph random walks (dpeaa)DE-He213 Al-Dhelaan, Mohammed verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 3(2014), 2 vom: 20. Aug., Seite 73-87 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:3 year:2014 number:2 day:20 month:08 pages:73-87 https://dx.doi.org/10.1007/s13748-014-0058-1 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_65 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_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_2232 GBV_ILN_2244 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 AR 3 2014 2 20 08 73-87 |
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10.1007/s13748-014-0058-1 doi (DE-627)SPR032252358 (SPR)s13748-014-0058-1-e DE-627 ger DE-627 rakwb eng 004 600 ASE Bellaachia, Abdelghani verfasserin aut Short text keyphrase extraction with hypergraphs 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. Keyphrase extraction (dpeaa)DE-He213 Hypergraph ranking (dpeaa)DE-He213 Text hypergraph (dpeaa)DE-He213 Hypergraph random walks (dpeaa)DE-He213 Al-Dhelaan, Mohammed verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 3(2014), 2 vom: 20. Aug., Seite 73-87 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:3 year:2014 number:2 day:20 month:08 pages:73-87 https://dx.doi.org/10.1007/s13748-014-0058-1 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_65 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_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_2232 GBV_ILN_2244 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 AR 3 2014 2 20 08 73-87 |
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10.1007/s13748-014-0058-1 doi (DE-627)SPR032252358 (SPR)s13748-014-0058-1-e DE-627 ger DE-627 rakwb eng 004 600 ASE Bellaachia, Abdelghani verfasserin aut Short text keyphrase extraction with hypergraphs 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. Keyphrase extraction (dpeaa)DE-He213 Hypergraph ranking (dpeaa)DE-He213 Text hypergraph (dpeaa)DE-He213 Hypergraph random walks (dpeaa)DE-He213 Al-Dhelaan, Mohammed verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 3(2014), 2 vom: 20. Aug., Seite 73-87 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:3 year:2014 number:2 day:20 month:08 pages:73-87 https://dx.doi.org/10.1007/s13748-014-0058-1 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_65 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_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_2232 GBV_ILN_2244 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 AR 3 2014 2 20 08 73-87 |
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Bellaachia, Abdelghani @@aut@@ Al-Dhelaan, Mohammed @@aut@@ |
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short text keyphrase extraction with hypergraphs |
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Short text keyphrase extraction with hypergraphs |
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Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. |
abstractGer |
Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. |
abstract_unstemmed |
Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach. |
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Short text keyphrase extraction with hypergraphs |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR032252358</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111201413.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13748-014-0058-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR032252358</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13748-014-0058-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">600</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bellaachia, Abdelghani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Short text keyphrase extraction with hypergraphs</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph. However, graphs by nature can only capture the pair-wise relation between vertices. Therefore, it is not clear if graphs can capture high-order relations of more than two words. In this paper, we propose to use a hypergraph to capture high-order relations appearing in short documents, and use such information to infer better ranking of words. Additionally, we model the temporal and social attributes of short documents and discriminative weights of words into the hypergraph as weights which give us the ability of capturing recent and topical keyphrases. Furthermore, to rank vertices in the proposed hypergraph, we propose a probabilistic random walk that takes into account weights of both vertices and hyperedges. We show the effectiveness of our approach by conducting extensive experiments over two different data sets which demonstrate the robustness of the proposed approach.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Keyphrase extraction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hypergraph ranking</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Text hypergraph</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hypergraph random walks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Al-Dhelaan, Mohammed</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Progress in artificial intelligence</subfield><subfield code="d">Berlin : Springer, 2012</subfield><subfield code="g">3(2014), 2 vom: 20. Aug., Seite 73-87</subfield><subfield code="w">(DE-627)718730933</subfield><subfield code="w">(DE-600)2668413-5</subfield><subfield code="x">2192-6360</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:2</subfield><subfield code="g">day:20</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:73-87</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s13748-014-0058-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" 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