{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:46:08Z","timestamp":1776786368148,"version":"3.51.2"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Inf Syst"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10844-022-00771-z","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T14:04:42Z","timestamp":1672149882000},"page":"421-452","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Computing semantic similarity of texts by utilizing dependency graph"],"prefix":"10.1007","volume":"61","author":[{"given":"Majid","family":"Mohebbi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyed Naser","family":"Razavi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Ali","family":"Balafar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"771_CR1","doi-asserted-by":"publisher","unstructured":"Bastings, J., Titov, I., Aziz, W., et al. (2017). Graph convolutional encoders for syntax-aware neural machine translation. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 1957\u20131967). Presented at the EMNLP 2017. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/D17-1209","DOI":"10.18653\/v1\/D17-1209"},{"key":"771_CR2","doi-asserted-by":"publisher","unstructured":"Bowman, S. R., Vilnis, L., Vinyals, O., et al. (2016). Generating sentences from a continuous space. In Proceedings of The 20th SIGNLL conference on computational natural language learning (pp. 10\u201321). Presented at the CoNLL 2016. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/K16-1002","DOI":"10.18653\/v1\/K16-1002"},{"key":"771_CR3","doi-asserted-by":"publisher","unstructured":"Cer, D., Diab, M., Agirre, E., et al. (2017). SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation. In Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017) (pp. 1\u201314). Presented at the SemEval 2017. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/S17-2001","DOI":"10.18653\/v1\/S17-2001"},{"key":"771_CR4","unstructured":"Conneau, A., & Lample, G. (2019). Cross-lingual language model pretraining. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d\u2019Alch\u00e9-Buc, E. Fox, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 32). Curran Associates, Inc.\u00a0https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf. Accessed 20 Jan 2022."},{"key":"771_CR5","doi-asserted-by":"publisher","unstructured":"Conneau, A., Kiela, D., Schwenk, H., et al. (2017). Supervised learning of universal sentence representations from natural language inference data. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 670\u2013680). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/D17-1070","DOI":"10.18653\/v1\/D17-1070"},{"key":"771_CR6","unstructured":"Dolan, B., & Brockett, C. (2005). Automatically constructing a corpus of sentential paraphrases. In Third International Workshop on Paraphrasing (IWP2005) (Third International Workshop on Paraphrasing (IWP2005)). Asia Federation of Natural Language Processing.\u00a0https:\/\/www.microsoft.com\/en-us\/research\/publication\/automatically-constructing-a-corpus-of-sentential-paraphrases\/. Accessed 6 Feb 2022."},{"key":"771_CR7","unstructured":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT (pp. 4171\u20134186). Presented at the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, Minnesota. https:\/\/aclanthology.org\/N19-1423.pdf.\u00a0Accessed 20 Jan 2022.\u00a0"},{"key":"771_CR8","unstructured":"Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., et al. (2015). Convolutional networks on graphs for learning molecular fingerprints. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 28). Curran Associates, Inc.\u00a0https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/f9be311e65d81a9ad8150a60844bb94c-Paper.pdf.\u00a0Accessed 30 Jan 2022."},{"key":"771_CR9","doi-asserted-by":"publisher","unstructured":"Gao, H., & Ji, S. (2021). Graph U-Nets. IEEE transactions on pattern analysis and machine intelligence. Presented at the IEEE Transactions on Pattern Analysis and Machine Intelligence. https:\/\/doi.org\/10.1109\/TPAMI.2021.3081010","DOI":"10.1109\/TPAMI.2021.3081010"},{"key":"771_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 1440\u20131448).","DOI":"10.1109\/ICCV.2015.169"},{"key":"771_CR11","doi-asserted-by":"publisher","unstructured":"He, H., & Lin, J. (2016). Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 937\u2013948). Presented at the NAACL-HLT 2016. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/N16-1108","DOI":"10.18653\/v1\/N16-1108"},{"key":"771_CR12","doi-asserted-by":"publisher","unstructured":"He, H., Gimpel, K., & Lin, J. (2015). Multi-perspective sentence similarity modeling with convolutional neural networks. In Proceedings of the 2015 conference on empirical methods in natural language processing (pp. 1576\u20131586). Presented at the EMNLP 2015, Lisbon, Portugal: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/D15-1181","DOI":"10.18653\/v1\/D15-1181"},{"key":"771_CR13","doi-asserted-by":"publisher","unstructured":"Iyyer, M., Manjunatha, V., Boyd-Graber, J., & Daum\u00e9 III, H. (2015). Deep unordered composition rivals syntactic methods for text classification. 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) (pp. 1681\u20131691). Presented at the ACL-IJCNLP 2015. Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/v1\/P15-1162","DOI":"10.3115\/v1\/P15-1162"},{"key":"771_CR14","unstructured":"Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. Presented at the International Conference on Learning Representations (ICLR) 2014, Banff, Canada. https:\/\/openreview.net\/forum?id=33X9fd2-9FyZd. Accessed 20 Jan 2022"},{"key":"771_CR15","unstructured":"Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In ICLR 2017. Presented at the 5th International Conference on Learning Representations, Palais des Congr\u00e8s Neptune, Toulon, France. Accessed 21 Jan 2022"},{"key":"771_CR16","unstructured":"Lan, Z., Chen, M., Goodman, S., et al. (2020). ALBERT: A Lite BERT for self-supervised learning of language representations. arXiv:1909.11942 [cs]. http:\/\/arxiv.org\/abs\/1909.11942. Accessed 5 Jan 2022"},{"key":"771_CR17","unstructured":"Liu, Y., Ott, M., Goyal, N., et al. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692 [cs]. http:\/\/arxiv.org\/abs\/1907.11692. Accessed 17 Feb 2022"},{"key":"771_CR18","unstructured":"Manning, C. D., Surdeanu, M., Bauer, J., et al. (2014). The Stanford CoreNLP natural language processing toolkit. In Association for Computational Linguistics (ACL) System Demonstrations (pp. 55\u201360). http:\/\/www.aclweb.org\/anthology\/P\/P14\/P14-5010. Accessed 25 Jan 2022."},{"key":"771_CR19","doi-asserted-by":"publisher","unstructured":"Marcheggiani, D., & Titov, I. (2017). Encoding sentences with graph convolutional networks for semantic role labeling. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 1506\u20131515). Presented at the EMNLP 2017. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/D17-1159","DOI":"10.18653\/v1\/D17-1159"},{"key":"771_CR20","doi-asserted-by":"publisher","unstructured":"Marelli, M., Bentivogli, L., Baroni, M., et al. (2014). SemEval-2014 task 1: evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (pp. 1\u20138). Presented at the SemEval 2014. Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/v1\/S14-2001","DOI":"10.3115\/v1\/S14-2001"},{"key":"771_CR21","doi-asserted-by":"publisher","unstructured":"Morishita, M., Oda, Y., Neubig, G., et al. (2017). An empirical study of mini-batch creation strategies for neural machine translation. In Proceedings of the first workshop on neural machine translation (pp. 61\u201368). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/W17-3208","DOI":"10.18653\/v1\/W17-3208"},{"key":"771_CR22","doi-asserted-by":"publisher","unstructured":"Mueller, J., & Thyagarajan, A. (2016). Siamese recurrent architectures for learning sentence similarity. In Proceedings of the thirtieth AAAI conference on artificial intelligence (pp. 2786\u20132792). AAAI Press. https:\/\/doi.org\/10.5555\/3016100.3016291","DOI":"10.5555\/3016100.3016291"},{"key":"771_CR23","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. In Empirical Methods in Natural Language Processing (EMNLP) (pp. 1532\u20131543). http:\/\/www.aclweb.org\/anthology\/D14-1162. Accessed 22 Jan 2020.\u00a0","DOI":"10.3115\/v1\/D14-1162"},{"key":"771_CR24","unstructured":"PyTorch Geometric. (n.d.). GitHub. https:\/\/github.com\/rusty1s\/pytorch_geometric. Accessed 20 Jan 2022."},{"key":"771_CR25","unstructured":"Rockt\u00e4schel, T., Grefenstette, E., Hermann, K. M., et al. (2015). Reasoning about entailment with neural attention. arXiv.org. https:\/\/arxiv.org\/abs\/1509.06664v4. Accessed 24 July 2021"},{"key":"771_CR26","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015 (pp. 234\u2013241). Springer International Publishing."},{"key":"771_CR27","doi-asserted-by":"publisher","unstructured":"Sennrich, R., Haddow, B., & Birch, A. (2016). Neural machine translation of rare words with subword units. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers) (pp. 1715\u20131725). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P16-1162","DOI":"10.18653\/v1\/P16-1162"},{"key":"771_CR28","unstructured":"Socher, R., Huang, E. H., Pennington, J., et al. (2011). Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In Proceedings of the 24th international conference on neural information processing systems (pp. 801\u2013809). Curran Associates Inc. Accessed 23 Oct 2022"},{"key":"771_CR29","unstructured":"Stanford CoreNLP. (n.d.). GitHub. https:\/\/github.com\/stanfordnlp\/CoreNLP. Accessed 20 July 2021"},{"key":"771_CR30","doi-asserted-by":"publisher","unstructured":"Tai, K. S., Socher, R., & Manning, C. D. (2015). Improved semantic representations from tree-structured long short-term memory networks. 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) (pp. 1556\u20131566). Presented at the ACL-IJCNLP 2015. Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/v1\/P15-1150","DOI":"10.3115\/v1\/P15-1150"},{"issue":"4","key":"771_CR31","doi-asserted-by":"publisher","first-page":"343","DOI":"10.3233\/WEB-190423","volume":"17","author":"KA Tarnowska","year":"2019","unstructured":"Tarnowska, K. A., & Ras, Z. W. (2019). Sentiment analysis of customer data. Web Intelligence, 17(4), 343\u2013363. https:\/\/doi.org\/10.3233\/WEB-190423","journal-title":"Web Intelligence"},{"issue":"1","key":"771_CR32","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/bdcc5010004","volume":"5","author":"KA Tarnowska","year":"2021","unstructured":"Tarnowska, K. A., & Ras, Z. W. (2021). NLP-Based Customer Loyalty Improvement Recommender System (CLIRS2). Big Data and Cognitive Computing, 5(1), 4. https:\/\/doi.org\/10.3390\/bdcc5010004","journal-title":"Big Data and Cognitive Computing"},{"issue":"6","key":"771_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102090","volume":"56","author":"NH Tien","year":"2019","unstructured":"Tien, N. H., Le, N. M., Tomohiro, Y., & Tatsuya, I. (2019). Sentence modeling via multiple word embeddings and multi-level comparison for semantic textual similarity. Information Processing & Management, 56(6), 102090. https:\/\/doi.org\/10.1016\/j.ipm.2019.102090","journal-title":"Information Processing & Management"},{"key":"771_CR34","unstructured":"Transformers. (n.d.). Transformers.. https:\/\/huggingface.co\/transformers\/v2.9.1\/. Accessed 20 Aug 2021"},{"key":"771_CR35","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 30). Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf. Accessed 8 Jan 2019"},{"key":"771_CR36","doi-asserted-by":"publisher","unstructured":"Wang, A., Singh, A., Michael, J., et al. (2018). GLUE: a multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and interpreting neural networks for NLP (pp. 353\u2013355). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/W18-5446","DOI":"10.18653\/v1\/W18-5446"},{"key":"771_CR37","doi-asserted-by":"crossref","unstructured":"Williams, A., Nangia, N., & Bowman, S. (2018). A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: Human language technologies, volume 1 (long papers) (pp. 1112\u20131122). Association for Computational Linguistics. http:\/\/aclweb.org\/anthology\/N18-1101. Accessed 20 Jan 2022","DOI":"10.18653\/v1\/N18-1101"},{"key":"771_CR38","doi-asserted-by":"publisher","unstructured":"Yang, Y., Yuan, S., Cer, D., et al. (2018). Learning semantic textual similarity from conversations. In Proceedings of the third workshop on representation learning for NLP (pp. 164\u2013174). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/W18-3022","DOI":"10.18653\/v1\/W18-3022"},{"key":"771_CR39","unstructured":"Yang, Z., Dai, Z., Yang, Y., et al. (2019). XLNet: Generalized autoregressive pretraining for language understanding. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d\u2019Alch\u00e9-Buc, E. Fox, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 32). Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/dc6a7e655d7e5840e66733e9ee67cc69-Paper.pdf.\u00a0Accessed 20 Aug 2021."},{"issue":"1","key":"771_CR40","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s10844-021-00663-8","volume":"58","author":"A \u017dagar","year":"2022","unstructured":"\u017dagar, A., & Robnik-\u0160ikonja, M. (2022). Cross-lingual transfer of abstractive summarizer to less-resource language. Journal of Intelligent Information Systems, 58(1), 153\u2013173. https:\/\/doi.org\/10.1007\/s10844-021-00663-8","journal-title":"Journal of Intelligent Information Systems"},{"key":"771_CR41","doi-asserted-by":"publisher","unstructured":"Zhang, X., Yang, Y., Yuan, S., et al. (2019). Syntax-infused variational autoencoder for text generation. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 2069\u20132078). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P19-1199","DOI":"10.18653\/v1\/P19-1199"},{"key":"771_CR42","unstructured":"Zhou, Y., Liu, C., & Pan, Y. (2016). Modelling sentence pairs with tree-structured attentive encoder. In Proceedings of COLING 2016, the 26th international conference on computational linguistics: Technical\u00a0papers (pp. 2912\u20132922). https:\/\/aclanthology.org\/C16-1274.\u00a0Accessed 20 Jan 2022."}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-022-00771-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-022-00771-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-022-00771-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T09:06:50Z","timestamp":1698052010000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-022-00771-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,27]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["771"],"URL":"https:\/\/doi.org\/10.1007\/s10844-022-00771-z","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,27]]},"assertion":[{"value":"19 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal ethics"}},{"value":"The authors declare that they have no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}