Combining weighted category-aware contextual information in convolutional neural networks for text classification
Convolutional neural networks (CNNs) are widely used in many natural language processing tasks, which employ some convolutional filters to capture useful semantic features of a text. However, a small window size convolutional filter is short of the ability to capture contextual information, simply i...
Saved in:
| Published in | World wide web (Bussum) Vol. 23; no. 5; pp. 2815 - 2834 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.09.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Convolutional neural networks (CNNs) are widely used in many natural language processing tasks, which employ some convolutional filters to capture useful semantic features of a text. However, a small window size convolutional filter is short of the ability to capture contextual information, simply increasing the window size may bring the problems of data sparsity and enormous parameters. To capture the contextual information, we propose to use the weighted sum operation to obtain contextual word representation. We present one implicit weighting method and two explicit category-aware weighting methods to assign the weights of the contextual information. Experimental results on five text classification datasets show the effectiveness of our proposed methods. |
|---|---|
| AbstractList | Convolutional neural networks (CNNs) are widely used in many natural language processing tasks, which employ some convolutional filters to capture useful semantic features of a text. However, a small window size convolutional filter is short of the ability to capture contextual information, simply increasing the window size may bring the problems of data sparsity and enormous parameters. To capture the contextual information, we propose to use the weighted sum operation to obtain contextual word representation. We present one implicit weighting method and two explicit category-aware weighting methods to assign the weights of the contextual information. Experimental results on five text classification datasets show the effectiveness of our proposed methods. |
| Author | Wu, Xin Li, Qing Cai, Yi Leung, Ho-fung Xu, Jingyun |
| Author_xml | – sequence: 1 givenname: Xin surname: Wu fullname: Wu, Xin organization: School of Software Engineering, South China University of Technology – sequence: 2 givenname: Yi surname: Cai fullname: Cai, Yi email: ycai@scut.edu.cn organization: School of Software Engineering, South China University of Technology – sequence: 3 givenname: Qing surname: Li fullname: Li, Qing organization: Department of Computing, The Hong Kong Polytechnic University – sequence: 4 givenname: Jingyun surname: Xu fullname: Xu, Jingyun organization: School of Software Engineering, South China University of Technology – sequence: 5 givenname: Ho-fung surname: Leung fullname: Leung, Ho-fung organization: Department of Computer Science and Engineering, The Chinese University of Hong Kong |
| BookMark | eNp9kE9PxCAQxYnRxN3VL-CpiWcUSlvo0Wz8l2ziRRNvhLK0snZhF6hrv720NTHxsKd5k5kf83hzcGqsUQBcYXSDEaK3HuOUIYhwCWObU9ifgBnOKYE4w-Q0asKKqPP3czD3foMQKkiJZ2C_tNtKG22a5KB08xHUOpEiqMa6HoqDcCqR1gT1HTrRJtrU1m1F0NZEPUy-bNsNbRwa1bmxhIN1nz6Jq8kAJrIV3utayxG8AGe1aL26_K0L8PZw_7p8gquXx-fl3QpKgssAFWW4oKSscC4KldJUUikYFRljLEdlXUX7kmUFSXOsCKlVyjKyZpXAdVULVpAFuJ7e3Tm775QPfGM7F416nmYxFJZlZRa30mlLOuu9UzXfOb0VrucY8SFaPkXLY7R8jJb3EWL_IKnD-LnghG6Po2RCfbxjGuX-XB2hfgDmE5OG |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2021_3071393 crossref_primary_10_1007_s11280_022_01102_6 |
| Cites_doi | 10.1109/TPAMI.2010.154 10.1023/A:1012491419635 10.18653/v1/D16-1053 10.1007/978-3-319-91452-7_56 10.3115/v1/D14-1167 10.3115/v1/P15-1010 10.3115/1072228.1072378 10.1609/aaai.v29i1.9513 10.18653/v1/P16-1228 10.3115/v1/N15-1140 10.18653/v1/D16-1244 10.1109/ICTAI.2015.57 10.18653/v1/P16-2034 10.3115/1118693.1118704 10.18653/v1/N16-1174 10.18653/v1/N16-1178 10.1038/323533a0 10.1145/1015330.1015435 10.1109/MIS.2019.2899142 10.18653/v1/D17-1201 10.1109/TPAMI.2008.110 10.1007/978-1-4614-3223-4_6 10.1007/s10579-005-7880-9 10.3115/v1/D14-1181 10.1108/eb026526 10.1007/978-3-540-45219-5_7 10.18653/v1/K15-1021 10.3115/v1/P14-2089 10.1016/j.eswa.2018.07.027 10.1109/TAFFC.2017.2716930 10.3115/1219840.1219855 10.24963/ijcai.2017/406 10.1007/978-3-030-02922-7_31 10.3115/1218955.1218990 10.1109/WI.2007.51 10.1016/j.im.2016.04.005 |
| ContentType | Journal Article |
| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020. |
| Copyright_xml | – notice: Springer Science+Business Media, LLC, part of Springer Nature 2020 – notice: Springer Science+Business Media, LLC, part of Springer Nature 2020. |
| DBID | AAYXX CITATION 3V. 7SC 7XB 8AL 8FD 8FE 8FG 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- L7M L~C L~D M0N P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U |
| DOI | 10.1007/s11280-019-00757-y |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database ProQuest Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic |
| DatabaseTitle | CrossRef Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) |
| DatabaseTitleList | Computer Science Database |
| Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1573-1413 |
| EndPage | 2834 |
| ExternalDocumentID | 10_1007_s11280_019_00757_y |
| GroupedDBID | -59 -5G -BR -EM -Y2 -~C .86 .DC .VR 06D 0R~ 0VY 123 1SB 203 29R 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5VS 67Z 6NX 8FE 8FG 8TC 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYOK AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BDATZ BENPR BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K6V K7- KDC KOV LAK LLZTM M0N M4Y MA- N2Q NB0 NPVJJ NQJWS NU0 O9- O93 O9J OAM OVD P2P P62 P9O PF0 PQQKQ PROAC PT4 PT5 QOS R89 R9I RIG RNI ROL RPX RSV RZC RZE RZK S16 S1Z S27 S3B SAP SCO SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TEORI TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7X Z83 Z88 ZMTXR ~A9 AAPKM AAYXX ABBRH ABDBE ABFSG ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT 7SC 7XB 8AL 8FD 8FK ABRTQ JQ2 L7M L~C L~D PKEHL PQEST PQGLB PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c319t-e7816739b15a6e272c7ca87a4888509fb391c8463251e33fe2843d8ba1fbfa863 |
| IEDL.DBID | U2A |
| ISSN | 1386-145X |
| IngestDate | Fri Jul 25 05:28:23 EDT 2025 Tue Jul 01 04:26:44 EDT 2025 Thu Apr 24 22:49:13 EDT 2025 Fri Feb 21 02:34:53 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | Convolutional neural networks Word representation Contextual information Text classification |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-e7816739b15a6e272c7ca87a4888509fb391c8463251e33fe2843d8ba1fbfa863 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2441384494 |
| PQPubID | 2034525 |
| PageCount | 20 |
| ParticipantIDs | proquest_journals_2441384494 crossref_primary_10_1007_s11280_019_00757_y crossref_citationtrail_10_1007_s11280_019_00757_y springer_journals_10_1007_s11280_019_00757_y |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20200900 2020-09-00 20200901 |
| PublicationDateYYYYMMDD | 2020-09-01 |
| PublicationDate_xml | – month: 9 year: 2020 text: 20200900 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationSubtitle | Internet and Web Information Systems |
| PublicationTitle | World wide web (Bussum) |
| PublicationTitleAbbrev | World Wide Web |
| PublicationYear | 2020 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Cotterell, R., Schütze, H.: Morphological word-embeddings. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1287–1292 (2015) Irsoy, O., Cardie, C.: Deep recursive neural networks for compositionality in language. In: Advances in Neural Information Processing Systems, pp. 2096–2104 (2014) Li, S., Zhao, Z., Liu, T., Hu, R., Du, X.: Initializing convolutional filters with semantic features for text classification. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1884–1889 (2017) QuanXWenyinLQiuBTerm weighting schemes for question categorizationIEEE Trans. Pattern Anal. Mach. Intell.20113351009102110.1109/TPAMI.2010.154 YangQRaoYXieHWangJWangFLChanWHCambriaECSegment-level joint topic-sentiment model for online review analysisIEEE Intell. Syst.2019341435010.1109/MIS.2019.2899142 Lan, M., Tan, C.L., Low, H.B.: Proposing a new term weighting scheme for text categorization. In: AAAI, vol. 6, pp. 763–768 (2006) Wu, X., Cai, Y., Li, Q., Xu, J., Leung, H.f.: Combining contextual information by self-attention mechanism in convolutional neural networks for text classification. In: International Conference on Web Information Systems Engineering, pp. 453–467. Springer (2018) Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 115–124. Association for Computational Linguistics (2005) Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013) RumelhartDEHintonGEWilliamsRJLearning representations by back-propagating errorsNature1986323608853310.1038/323533a0 Socher, R., Bauer, J., Manning, C.D., et al.: Parsing with compositional vector grammars. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 455–465 (2013) Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., Xu, B.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 207–212 (2016) CoverTMThomasJAElements of Information Theory2012New YorkWiley0762.94001 Socher, R., Lin, C.C., Manning, C., Ng, A.Y.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 129–136 (2011) Wang, S., Manning, C.D.: Baselines and bigrams: Simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 90–94. Association for Computational Linguistics (2012) Zhang, Y., Roller, S., Wallace, B.: Mgnc-cnn: a simple approach to exploiting multiple word embeddings for sentence classification. arXiv:1603.00968 (2016) Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading, arXiv:1601.06733 (2016) CollobertRWestonJBottouLKarlenMKavukcuogluKKuksaPNatural language processing (almost) from scratchJ. Mach. Learn. Res.201112Aug249325371280.68161 Hu, Z., Ma, X., Liu, Z., Hovy, E., Xing, E.: Harnessing deep neural networks with logic rules, arXiv:1603.06318 (2016) Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: AAAI, vol. 333, pp. 2267–2273 (2015) LiangWXieHRaoYLauRYWangFLUniversal affective model for readers’ emotion classification over short textsExpert Syst. Appl.201811432233310.1016/j.eswa.2018.07.027 Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2915–2921. AAAI Press (2017) Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009) Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 6000–6010 (2017) Li, X., Roth, D.: Learning question classifiers. In: Proceedings of the 19th international conference on Computational linguistics, vol. 1, pp. 1–7. Association for Computational Linguistics (2002) Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the twenty-first international conference on Machine learning, p 78. ACM (2004) Yin, W., Schütze, H.: Multichannel variable-size convolution for sentence classification. arXiv:1603.04513 (2016) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012) Post, M., Bergsma, S.: Explicit and implicit syntactic features for text classification. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 866–872 (2013) Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate, arXiv:1409.0473 (2014) Wang, T., Cai, Y., Leung, H.f., Cai, Z., Min, H.: Entropy-based term weighting schemes for text categorization in vsm. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 325–332 (2015) LeCunYBengioYConvolutional networks for images, speech, and time seriesHandbook Brain Theory Neural Netw.19953361101995 Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002) RaoYXieHLiJJinFWangFLLiQSocial emotion classification of short text via topic-level maximum entropy modelInform. Manag.201653897898610.1016/j.im.2016.04.005 Tang, D., Qin, B., Feng, X., Liu, T.: Target-dependent sentiment classification with long short term memory, arXiv:1512.01100 (2015) Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Mining Text Data, pp. 163–222. Springer (2012) WiebeJWilsonTCardieCAnnotating expressions of opinions and emotions in languageLang. Resour. Eval.2005392–316521010.1007/s10579-005-7880-9 LanMTanCLSuJLuYSupervised and traditional term weighting methods for automatic text categorizationIEEE Trans. Pattern Anal. Mach. Intell.200931472173510.1109/TPAMI.2008.110 Zeiler, M.D.: Adadelta: An adaptive learning rate method. arXiv:1212.5701 (2012) Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649–657 (2015) Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph and text jointly embedding. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1591–1601 (2014) Li, Y., Cai, Y., Leung, H.F., Li, Q.: Improving short text modeling by two-level attention networks for sentiment classification. In: International Conference on Database Systems for Advanced Applications, pp. 878–890. Springer (2018) Santos, C.D., Zadrozny, B.: Learning character-level representations for part-of-speech tagging. In: Proceedings of the 31st International Conference on Machine Learning (ICML-14), pp. 1818–1826 (2014) Kim, Y.: Convolutional neural networks for sentence classification, arXiv:1408.5882 (2014) LiXRaoYXieHLauRYKYinJWangFLBootstrapping social emotion classification with semantically rich hybrid neural networksIEEE Trans. Affect. Comput.20178442844210.1109/TAFFC.2017.2716930 Parikh, A.P., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference, arXiv:1606.01933(2016) Sparck JonesKA statistical interpretation of term specificity and its application in retrievalJ. Document.1972281112110.1108/eb026526 Yu, M., Dredze, M.: Improving lexical embeddings with semantic knowledge. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 545–550 (2014) Turian, J., Ratinov, L., Bengio, Y.: Word representations: A simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394. Association for Computational Linguistics (2010) Chen, X., Xu, L., Liu, Z., Sun, M., Luan, H.: Joint learning of character and word embeddings. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015) Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, p 271. Association for Computational Linguistics (2004) Yang, C., Lin, K.H.Y., Chen, H.H.: Emotion classification using Web blog corpora. In: IEEE/WIC/ACM International Conference on Web Intelligence (WI’07), pp. 275–278 (2007) Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016) Debole, F., Sebastiani, F.: Supervised term weighting for automated text 757_CR19 757_CR15 757_CR14 TM Cover (757_CR7) 2012 757_CR13 757_CR57 757_CR12 757_CR56 757_CR11 757_CR55 757_CR10 Q Yang (757_CR51) 2019; 34 757_CR54 757_CR53 757_CR52 Y Rao (757_CR33) 2016; 53 757_CR50 757_CR8 757_CR6 757_CR9 757_CR49 757_CR48 757_CR46 757_CR45 757_CR44 757_CR43 X Li (757_CR21) 2017; 8 757_CR42 757_CR41 757_CR40 Y LeCun (757_CR17) 1995; 3361 DE Rumelhart (757_CR34) 1986; 323 K Sparck Jones (757_CR39) 1972; 28 R Collobert (757_CR5) 2011; 12 757_CR38 757_CR37 J Wiebe (757_CR47) 2005; 39 757_CR36 757_CR35 757_CR31 757_CR30 W Liang (757_CR23) 2018; 114 E Leopold (757_CR18) 2002; 46 757_CR29 757_CR3 757_CR28 757_CR4 757_CR27 757_CR1 757_CR26 757_CR2 757_CR25 757_CR24 757_CR22 757_CR20 X Quan (757_CR32) 2011; 33 M Lan (757_CR16) 2009; 31 |
| References_xml | – reference: Chen, X., Xu, L., Liu, Z., Sun, M., Luan, H.: Joint learning of character and word embeddings. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015) – reference: Santos, C.D., Zadrozny, B.: Learning character-level representations for part-of-speech tagging. In: Proceedings of the 31st International Conference on Machine Learning (ICML-14), pp. 1818–1826 (2014) – reference: CoverTMThomasJAElements of Information Theory2012New YorkWiley0762.94001 – reference: Turian, J., Ratinov, L., Bengio, Y.: Word representations: A simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394. Association for Computational Linguistics (2010) – reference: Yang, C., Lin, K.H.Y., Chen, H.H.: Emotion classification using Web blog corpora. In: IEEE/WIC/ACM International Conference on Web Intelligence (WI’07), pp. 275–278 (2007) – reference: Yu, M., Dredze, M.: Improving lexical embeddings with semantic knowledge. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 545–550 (2014) – reference: WiebeJWilsonTCardieCAnnotating expressions of opinions and emotions in languageLang. Resour. Eval.2005392–316521010.1007/s10579-005-7880-9 – reference: Post, M., Bergsma, S.: Explicit and implicit syntactic features for text classification. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 866–872 (2013) – reference: Debole, F., Sebastiani, F.: Supervised term weighting for automated text categorization. In: Text Mining and its Applications, pp. 81–97. Springer (2004) – reference: Wu, X., Cai, Y., Li, Q., Xu, J., Leung, H.f.: Combining contextual information by self-attention mechanism in convolutional neural networks for text classification. In: International Conference on Web Information Systems Engineering, pp. 453–467. Springer (2018) – reference: Cotterell, R., Schütze, H.: Morphological word-embeddings. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1287–1292 (2015) – reference: Zhang, Y., Roller, S., Wallace, B.: Mgnc-cnn: a simple approach to exploiting multiple word embeddings for sentence classification. arXiv:1603.00968 (2016) – reference: Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., Xu, B.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 207–212 (2016) – reference: Yin, W., Schütze, H.: Multichannel variable-size convolution for sentence classification. arXiv:1603.04513 (2016) – reference: Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: AAAI, vol. 333, pp. 2267–2273 (2015) – reference: Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, p 271. Association for Computational Linguistics (2004) – reference: Irsoy, O., Cardie, C.: Deep recursive neural networks for compositionality in language. In: Advances in Neural Information Processing Systems, pp. 2096–2104 (2014) – reference: Zeiler, M.D.: Adadelta: An adaptive learning rate method. arXiv:1212.5701 (2012) – reference: Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate, arXiv:1409.0473 (2014) – reference: Sparck JonesKA statistical interpretation of term specificity and its application in retrievalJ. Document.1972281112110.1108/eb026526 – reference: Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016) – reference: CollobertRWestonJBottouLKarlenMKavukcuogluKKuksaPNatural language processing (almost) from scratchJ. Mach. Learn. Res.201112Aug249325371280.68161 – reference: Tang, D., Qin, B., Feng, X., Liu, T.: Target-dependent sentiment classification with long short term memory, arXiv:1512.01100 (2015) – reference: Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the twenty-first international conference on Machine learning, p 78. ACM (2004) – reference: LanMTanCLSuJLuYSupervised and traditional term weighting methods for automatic text categorizationIEEE Trans. Pattern Anal. Mach. Intell.200931472173510.1109/TPAMI.2008.110 – reference: Li, Y., Cai, Y., Leung, H.F., Li, Q.: Improving short text modeling by two-level attention networks for sentiment classification. In: International Conference on Database Systems for Advanced Applications, pp. 878–890. Springer (2018) – reference: QuanXWenyinLQiuBTerm weighting schemes for question categorizationIEEE Trans. Pattern Anal. Mach. Intell.20113351009102110.1109/TPAMI.2010.154 – reference: Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013) – reference: Li, S., Zhao, Z., Liu, T., Hu, R., Du, X.: Initializing convolutional filters with semantic features for text classification. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1884–1889 (2017) – reference: Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649–657 (2015) – reference: Wang, T., Cai, Y., Leung, H.f., Cai, Z., Min, H.: Entropy-based term weighting schemes for text categorization in vsm. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 325–332 (2015) – reference: Hu, Z., Ma, X., Liu, Z., Hovy, E., Xing, E.: Harnessing deep neural networks with logic rules, arXiv:1603.06318 (2016) – reference: LiXRaoYXieHLauRYKYinJWangFLBootstrapping social emotion classification with semantically rich hybrid neural networksIEEE Trans. Affect. Comput.20178442844210.1109/TAFFC.2017.2716930 – reference: Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 115–124. Association for Computational Linguistics (2005) – reference: RaoYXieHLiJJinFWangFLLiQSocial emotion classification of short text via topic-level maximum entropy modelInform. Manag.201653897898610.1016/j.im.2016.04.005 – reference: Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph and text jointly embedding. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1591–1601 (2014) – reference: RumelhartDEHintonGEWilliamsRJLearning representations by back-propagating errorsNature1986323608853310.1038/323533a0 – reference: Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002) – reference: Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009) – reference: LeCunYBengioYConvolutional networks for images, speech, and time seriesHandbook Brain Theory Neural Netw.19953361101995 – reference: Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Mining Text Data, pp. 163–222. Springer (2012) – reference: Iacobacci, I., Pilehvar, M.T., Navigli, R.: Sensembed: learning sense embeddings for word and relational similarity. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 95–105 (2015) – reference: Kim, Y.: Convolutional neural networks for sentence classification, arXiv:1408.5882 (2014) – reference: Socher, R., Bauer, J., Manning, C.D., et al.: Parsing with compositional vector grammars. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 455–465 (2013) – reference: YangQRaoYXieHWangJWangFLChanWHCambriaECSegment-level joint topic-sentiment model for online review analysisIEEE Intell. Syst.2019341435010.1109/MIS.2019.2899142 – reference: Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013) – reference: Li, X., Roth, D.: Learning question classifiers. In: Proceedings of the 19th international conference on Computational linguistics, vol. 1, pp. 1–7. Association for Computational Linguistics (2002) – reference: Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 6000–6010 (2017) – reference: Wang, S., Manning, C.D.: Baselines and bigrams: Simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 90–94. Association for Computational Linguistics (2012) – reference: Lan, M., Tan, C.L., Low, H.B.: Proposing a new term weighting scheme for text categorization. In: AAAI, vol. 6, pp. 763–768 (2006) – reference: LiangWXieHRaoYLauRYWangFLUniversal affective model for readers’ emotion classification over short textsExpert Syst. Appl.201811432233310.1016/j.eswa.2018.07.027 – reference: Socher, R., Lin, C.C., Manning, C., Ng, A.Y.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 129–136 (2011) – reference: Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012) – reference: Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading, arXiv:1601.06733 (2016) – reference: Parikh, A.P., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference, arXiv:1606.01933(2016) – reference: Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2915–2921. AAAI Press (2017) – reference: LeopoldEKindermannJText categorization with support vector machines. How to represent texts in input space?Mach. Learn.2002461-342344410.1023/A:1012491419635 – volume: 33 start-page: 1009 issue: 5 year: 2011 ident: 757_CR32 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2010.154 – ident: 757_CR11 – volume: 46 start-page: 423 issue: 1-3 year: 2002 ident: 757_CR18 publication-title: Mach. Learn. doi: 10.1023/A:1012491419635 – ident: 757_CR4 doi: 10.18653/v1/D16-1053 – ident: 757_CR22 doi: 10.1007/978-3-319-91452-7_56 – ident: 757_CR15 – ident: 757_CR44 doi: 10.3115/v1/D14-1167 – ident: 757_CR10 doi: 10.3115/v1/P15-1010 – ident: 757_CR38 – ident: 757_CR43 – ident: 757_CR19 doi: 10.3115/1072228.1072378 – ident: 757_CR24 – ident: 757_CR14 doi: 10.1609/aaai.v29i1.9513 – ident: 757_CR9 doi: 10.18653/v1/P16-1228 – ident: 757_CR31 – volume: 12 start-page: 2493 issue: Aug year: 2011 ident: 757_CR5 publication-title: J. Mach. Learn. Res. – ident: 757_CR6 doi: 10.3115/v1/N15-1140 – ident: 757_CR30 doi: 10.18653/v1/D16-1244 – ident: 757_CR35 – ident: 757_CR45 doi: 10.1109/ICTAI.2015.57 – ident: 757_CR3 – ident: 757_CR57 doi: 10.18653/v1/P16-2034 – ident: 757_CR25 – ident: 757_CR29 doi: 10.3115/1118693.1118704 – ident: 757_CR50 doi: 10.18653/v1/N16-1174 – ident: 757_CR56 doi: 10.18653/v1/N16-1178 – ident: 757_CR42 – volume: 323 start-page: 533 issue: 6088 year: 1986 ident: 757_CR34 publication-title: Nature doi: 10.1038/323533a0 – ident: 757_CR26 doi: 10.1145/1015330.1015435 – volume: 34 start-page: 43 issue: 1 year: 2019 ident: 757_CR51 publication-title: IEEE Intell. Syst. doi: 10.1109/MIS.2019.2899142 – ident: 757_CR20 doi: 10.18653/v1/D17-1201 – ident: 757_CR13 – volume: 31 start-page: 721 issue: 4 year: 2009 ident: 757_CR16 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.110 – ident: 757_CR36 – ident: 757_CR2 – ident: 757_CR55 – volume: 3361 start-page: 1995 issue: 10 year: 1995 ident: 757_CR17 publication-title: Handbook Brain Theory Neural Netw. – ident: 757_CR1 doi: 10.1007/978-1-4614-3223-4_6 – volume: 39 start-page: 165 issue: 2–3 year: 2005 ident: 757_CR47 publication-title: Lang. Resour. Eval. doi: 10.1007/s10579-005-7880-9 – ident: 757_CR12 doi: 10.3115/v1/D14-1181 – volume: 28 start-page: 11 issue: 1 year: 1972 ident: 757_CR39 publication-title: J. Document. doi: 10.1108/eb026526 – ident: 757_CR8 doi: 10.1007/978-3-540-45219-5_7 – ident: 757_CR41 – ident: 757_CR52 doi: 10.18653/v1/K15-1021 – ident: 757_CR53 doi: 10.3115/v1/P14-2089 – volume: 114 start-page: 322 year: 2018 ident: 757_CR23 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.07.027 – volume: 8 start-page: 428 issue: 4 year: 2017 ident: 757_CR21 publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/TAFFC.2017.2716930 – ident: 757_CR28 doi: 10.3115/1219840.1219855 – ident: 757_CR46 doi: 10.24963/ijcai.2017/406 – ident: 757_CR48 doi: 10.1007/978-3-030-02922-7_31 – ident: 757_CR27 doi: 10.3115/1218955.1218990 – ident: 757_CR37 – ident: 757_CR54 – ident: 757_CR49 doi: 10.1109/WI.2007.51 – volume-title: Elements of Information Theory year: 2012 ident: 757_CR7 – volume: 53 start-page: 978 issue: 8 year: 2016 ident: 757_CR33 publication-title: Inform. Manag. doi: 10.1016/j.im.2016.04.005 – ident: 757_CR40 |
| SSID | ssj0006391 |
| Score | 2.2300117 |
| Snippet | Convolutional neural networks (CNNs) are widely used in many natural language processing tasks, which employ some convolutional filters to capture useful... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2815 |
| SubjectTerms | Artificial neural networks Classification Computer Science Database Management Information Systems Applications (incl.Internet) Natural language processing Neural networks Operating Systems Special Issue on Web Information Systems Engineering 2018 Weighting methods |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LSwMxEA7aXvQgPrFaZQ_eNNjsZjfZk6i0FMEiYqG3JclmT9J3Lf33zmSzrQr2tAvJ5DCTeSSZ-YaQm9AqniYqpwjEQrloCapToUHxWCQt00a7QtrXXtLt85dBPPAXbjOfVlnZRGeo85HBO_J7cENAzXnKH8YTil2j8HXVt9DYJXUwwVLWSP2p3Xt7X9ti8L_lkUsmlPF44MtmyuI5oMCkrJSi3xR09ds1beLNP0-kzvN0DsmBDxmDx1LGR2THDo_J_g8gwRMyAbXWrtVDsHR3nTYPMNUJy0-oWqqpDTAnHQzxAlbyYKkoEvjHkS-_AWEQAS7dx6WHzwKYGiBhYDDOxsQiR3hK-p32x3OX-mYK1ICWzakVkiUiSjWLVWJDERphlBQKFFhC0FBo4JSBYCSCgMdGUWHBb0W51IoVulAyic5IbTga2nMS6ChHEEUJyq65EmlqYyNYDqddXphWbBqEVXzMjEcax4YXn9kGIxl5nwHvM8f7bNUgt2uacYmzsXV2sxJP5nVulm12SIPcVSLbDP-_2sX21S7JXoiHbJdY1iS1-XRhryASmetrv92-AffD2-o priority: 102 providerName: ProQuest |
| Title | Combining weighted category-aware contextual information in convolutional neural networks for text classification |
| URI | https://link.springer.com/article/10.1007/s11280-019-00757-y https://www.proquest.com/docview/2441384494 |
| Volume | 23 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwED5BWWDgjSiPygMbWCKJEztjQS0IBEKISmWKbMeZUIG2gPrvuXMdCgiQmBzJZw8-3yv-7g7gIHZa5JkuORVi4UIeS25yaVDwokS5yFjjE2mvrrPznrjop_2QFDaq0e71k6TX1LNkN1SlBKLKOdk5ySfzsJBi7E5Arl7c_tC_aHOnYZbKeCTSfkiV-XmPr-Zo5mN-exb11qa7CsvBTWTtKV_XYM4N1mGlbsHAgkSuw9KneoIb8IwExnd8YG_-l6crGSGeKAuF6zc9dIyg6aiPX3DzUDOVOIPfNPMa7iFOUp1LP3iU-IghKaOFzJK7Tfgiv3ATet3O3ek5Dz0VuEVhG3MnVZTJJDdRqjMXy9hKq5XUKMcKfYfK4OFZ9EkS9HtcklQOzVdSKqOjylRaZckWNAaPA7cNzCQl1VJUKPNGaJnnLrUyKjHoFZU9Tm0TovpoCxsKjlPfi4diViqZ2FEgOwrPjmLShMOPNU_Tcht_Uu_VHCuC6I0K9FeQ9ULkoglHNRdn07_vtvM_8l1YjCn29nizPWiMhy9uHx2UsWnBvOqetWChfXZ_2cHxpHN9c9vyt_QdaiXiVw |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07T-QwEB7xKIAC8RTL43ABFVjg2ImTAp1Od-wtx6MCabtgO06FdoFdWO2f4jcy4yQsIB0dVSLZ42L8eT4_5gGwF3mjssQUnBKxcKWPNbeZtrjwhEy9sM6GQNrLq6Rzo_514-4UvDSxMORW2djEYKiLvqM78iOkIZRWKlM_7x84VY2i19WmhEYFi3M_HuGRbXBy9gfndz-K2qfXvzu8rirAHcJtyL1ORaJlZkVsEh_pyGlnUm0QySmyZ2llJhyyskTm91KWHg24LFJrRGlLkyYSx52GWSWRySkyvf33zfIj21cHvDThQsXdOkinCtVDIiAXsIwTS2s-_kiEk93tpwfZwHPtJVisN6jsV4WoZZjyvRVYeJe2cBUe0IjYUFiCjcLNqi8YOVZRsAs3I_PoGXnAo9l_wpHq1KwEAPynluca7thI6TTDJzijDxh2ZSTIHO3qyY0pCK7BzbcoeR1mev2e3wBmZUEpG1M0LVYZnWU-dloUeLZWpTuOXQtEo8fc1XnNqbzGXT7JyEy6z1H3edB9Pm7BwZvMfZXV48ve28305PUKH-QTPLbgsJmySfP_R9v8erRdmOtcX17kF2dX51swH9HxPri0bcPM8PHJ7-AeaGh_BOAxuP1upL8Cf4wWUw |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwEB7BIiF6QBSKugVaH9oTWODYiZMDqtrCCgpdIVSkvQXbcU7V8tiF1f41fh0zjsPSSuXGKZEc-zD-PN-MMw-Az4k3qshMxakQC1d6T3NbaIsHT8jcC-tsSKT91c-OLtTPQTqYg4c2F4bCKludGBR1deXojnwXaQhnK1Wo3TqGRZwd9L5e33DqIEV_Wtt2Gg1ETvx0gu7baP_4APf6S5L0Dn__OOKxwwB3CL0x9zoXmZaFFanJfKITp53JtUFU58iktZWFcMjQEq0AL2XtUZnLKrdG1LY2eSZx3XlY0OQVdWDh-2H_7PyJB5D7G3cvz7hQ6SCm7DSJe0gLFBBWcOJszad_0-LM1v3n92xgvd4KLEdzlX1r8PUW5vxwFd48K2K4BjeoUmxoM8Em4Z7VV4zCrCj1hZuJufWM4uFRkne4UizUSnDAdxq5j-DHQSquGR4hNH3E8FNGE5kjG5-CmsLEd3DxKmJeh87waujfA7OyogKOOSoaq4wuCp86LSr0tFXt9lLXBdHKsXSxyjk12_hTzuozk-xLlH0ZZF9Ou7D9NOe6qfHx4teb7faU8byPyhk6u7DTbtls-P-rfXh5tU-wiCgvT4_7JxuwlJCvH-LbNqEzvr3zW2gQje3HiDwGl68N9kchQRvl |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Combining+weighted+category-aware+contextual+information+in+convolutional+neural+networks+for+text+classification&rft.jtitle=World+wide+web+%28Bussum%29&rft.au=Wu%2C+Xin&rft.au=Cai%2C+Yi&rft.au=Li%2C+Qing&rft.au=Xu%2C+Jingyun&rft.date=2020-09-01&rft.pub=Springer+US&rft.issn=1386-145X&rft.eissn=1573-1413&rft.volume=23&rft.issue=5&rft.spage=2815&rft.epage=2834&rft_id=info:doi/10.1007%2Fs11280-019-00757-y&rft.externalDocID=10_1007_s11280_019_00757_y |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1386-145X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1386-145X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1386-145X&client=summon |