{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T16:53:37Z","timestamp":1771606417284,"version":"3.50.1"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"crossref","award":["833647"],"award-info":[{"award-number":["833647"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"crossref","award":["825619"],"award-info":[{"award-number":["825619"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Alma Mater Studiorum - Universit\u00e0 di Bologna"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Law"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes.<\/jats:p>","DOI":"10.1007\/s10506-021-09288-2","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T18:02:37Z","timestamp":1620756157000},"page":"59-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Detecting and explaining unfairness in consumer contracts through memory networks"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1697-8586","authenticated-orcid":false,"given":"Federico","family":"Ruggeri","sequence":"first","affiliation":[]},{"given":"Francesca","family":"Lagioia","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Lippi","sequence":"additional","affiliation":[]},{"given":"Paolo","family":"Torroni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"9288_CR1","doi-asserted-by":"publisher","first-page":"e93","DOI":"10.7717\/peerj-cs.93","volume":"2","author":"N Aletras","year":"2016","unstructured":"Aletras N, Tsarapatsanis D, Preo\u0163iuc-Pietro D, Lampos V (2016) Predicting judicial decisions of the European court of human rights: a natural language processing perspective. PeerJ Comput Sci 2:e93","journal-title":"PeerJ Comput Sci"},{"key":"9288_CR2","doi-asserted-by":"crossref","unstructured":"Amith M, Zhang Y, Xu H, Tao C (2017) Knowledge-based approach for named entity recognition in biomedical literature: a use case in biomedical software identification. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer, pp 386\u2013395","DOI":"10.1007\/978-3-319-60045-1_40"},{"key":"9288_CR3","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta AB, D\u00edaz-Rodr\u00edguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Garc\u00eda S, Gil-L\u00f3pez S, Molina D, Benjamins R et al (2020) Explainable artificial intelligence (xai): concepts, taxonomies, opportunities and challenges toward responsible ai. Inf Fusion 58:82\u2013115","journal-title":"Inf Fusion"},{"key":"9288_CR4","doi-asserted-by":"publisher","unstructured":"Ashley KD (2017) Artificial intelligence and legal analytics: new tools for law practice in the digital age. Cambridge University Press. https:\/\/doi.org\/10.1017\/9781316761380","DOI":"10.1017\/9781316761380"},{"key":"9288_CR5","doi-asserted-by":"crossref","unstructured":"Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) Dbpedia: a nucleus for a web of open data. In: The semantic web. Springer, pp 722\u2013735","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"9288_CR6","doi-asserted-by":"crossref","unstructured":"Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. In: Neural networks: tricks of the trade. Springer, pp 437\u2013478","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"9288_CR7","doi-asserted-by":"publisher","unstructured":"Biagioli C, Francesconi E, Passerini A, Montemagni S, Soria C (2005) Automatic semantics extraction in law documents. In: Proceedings of the 10th international conference on Artificial intelligence and law (ICAIL '05). Association for Computing Machinery, New York, NY, USA, pp 133\u2013140. https:\/\/doi.org\/10.1145\/1165485.1165506","DOI":"10.1145\/1165485.1165506"},{"key":"9288_CR8","doi-asserted-by":"crossref","unstructured":"Bian J, Gao B, Liu TY (2014) Knowledge-powered deep learning for word embedding. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 132\u2013148","DOI":"10.1007\/978-3-662-44848-9_9"},{"key":"9288_CR9","unstructured":"Biran O, Cotton C (2017) Explanation and justification in machine learning: A survey. In: IJCAI-17 workshop on explainable AI (XAI), vol 8, no 1, pp 8\u201313"},{"issue":"6","key":"9288_CR10","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1177\/0165551515602846","volume":"41","author":"M Bohlouli","year":"2015","unstructured":"Bohlouli M, Dalter J, Dornh\u00f6fer M, Zenkert J, Fathi M (2015) Knowledge discovery from social media using big data-provided sentiment analysis (somabit). J Inf Sci 41(6):779\u2013798","journal-title":"J Inf Sci"},{"key":"9288_CR11","doi-asserted-by":"crossref","unstructured":"Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data, pp 1247\u20131250","DOI":"10.1145\/1376616.1376746"},{"key":"9288_CR12","unstructured":"Bordes A, Weston J (2016) Learning end-to-end goal-oriented dialog. CoRR. arXiv:abs\/1605.07683"},{"key":"9288_CR13","unstructured":"Bordes A, Usunier N, Chopra S, Weston J (2015) Large-scale simple question answering with memory networks. ArXiv preprint arXiv:150602075"},{"key":"9288_CR14","unstructured":"Bordes A, Boureau YL, Weston J (2016) Learning end-to-end goal-oriented dialog. ArXiv preprint arXiv:160507683"},{"key":"9288_CR15","doi-asserted-by":"crossref","unstructured":"Bowman SR, Potts C, Manning CD (2014) Recursive neural networks can learn logical semantics. ArXiv preprint arXiv:14061827","DOI":"10.18653\/v1\/W15-4002"},{"key":"9288_CR16","unstructured":"Braun D (2018) Customer-centered LegalTech: automated analysis of standard form contracts"},{"key":"9288_CR17","doi-asserted-by":"crossref","unstructured":"Callan J, Mitamura T (2002) Knowledge-based extraction of named entities. In: Proceedings of the 11th international conference on information and knowledge management, pp 532\u2013537","DOI":"10.1145\/584792.584880"},{"key":"9288_CR18","doi-asserted-by":"crossref","unstructured":"Cambria E, Olsher D, Rajagopal D (2014) Senticnet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: 28th AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v28i1.8928"},{"key":"9288_CR19","unstructured":"Chandar S, Ahn S, Larochelle H, Vincent P, Tesauro G, Bengio Y (2016) Hierarchical memory networks. ArXiv preprint arXiv:160507427"},{"key":"9288_CR20","first-page":"6244","volume":"33","author":"J Chen","year":"2019","unstructured":"Chen J, Chen J, Yu Z (2019) Incorporating structured commonsense knowledge in story completion. Proc AAAI Conf Artif Intell 33:6244\u20136251","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"9288_CR21","doi-asserted-by":"crossref","unstructured":"Cheng J, Dong L, Lapata M (2016) Long short-term memory-networks for machine reading. ArXiv preprint arXiv:160106733","DOI":"10.18653\/v1\/D16-1053"},{"key":"9288_CR22","doi-asserted-by":"crossref","unstructured":"Choi E, Bahadori MT, Song L, Stewart WF, Sun J (2017) Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 787\u2013795","DOI":"10.1145\/3097983.3098126"},{"key":"9288_CR23","doi-asserted-by":"publisher","unstructured":"Contissa G, Docter K, Lagioia F, Lippi M, Micklitz HW, Palka P, Sartor G, Torroni P (2018) Automated processing of privacy policies under the EU general data protection regulation. In: Legal knowledge and information systems: JURIX 2018: the 31st annual conference, frontiers in artificial intelligence and applications, vol 313. IOS Press, pp 51\u201360. https:\/\/doi.org\/10.3233\/978-1-61499-935-5-51","DOI":"10.3233\/978-1-61499-935-5-51"},{"issue":"5","key":"9288_CR24","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s11257-008-9051-3","volume":"18","author":"H Cramer","year":"2008","unstructured":"Cramer H, Evers V, Ramlal S, Van Someren M, Rutledge L, Stash N, Aroyo L, Wielinga B (2008) The effects of transparency on trust in and acceptance of a content-based art recommender. User Model User-Adapted Interact 18(5):455","journal-title":"User Model User-Adapted Interact"},{"key":"9288_CR25","doi-asserted-by":"crossref","unstructured":"Dadas S (2019) Combining neural and knowledge-based approaches to named entity recognition in polish. In: International conference on artificial intelligence and soft computing. Springer, pp 39\u201350","DOI":"10.1007\/978-3-030-20912-4_4"},{"key":"9288_CR26","doi-asserted-by":"crossref","unstructured":"Dekhili G, Le NT, Sadat F (2019) Augmenting named entity recognition with commonsense knowledge. In: Proceedings of the 2019 workshop on widening NLP","DOI":"10.1007\/978-3-030-30639-7_2"},{"key":"9288_CR27","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. ArXiv preprint arXiv:181004805"},{"key":"9288_CR28","doi-asserted-by":"crossref","unstructured":"Dhingra B, Liu H, Yang Z, Cohen WW, Salakhutdinov R (2016) Gated-attention readers for text comprehension. ArXiv preprint arXiv:160601549","DOI":"10.18653\/v1\/P17-1168"},{"key":"9288_CR29","unstructured":"Doran D, Schulz S, Besold TR (2017) What does explainable ai really mean? A new conceptualization of perspectives. ArXiv preprint arXiv:171000794"},{"key":"9288_CR30","doi-asserted-by":"crossref","unstructured":"Fabian B, Ermakova T, Lentz T (2017) Large-scale readability analysis of privacy policies. In: Proceedings of the international conference on web intelligence, pp 18\u201325","DOI":"10.1145\/3106426.3106427"},{"issue":"4","key":"9288_CR31","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s11023-018-9482-5","volume":"28","author":"L Floridi","year":"2018","unstructured":"Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C, Madelin R, Pagallo U, Rossi F et al (2018) Ai4people-an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds Mach 28(4):689\u2013707","journal-title":"Minds Mach"},{"issue":"7626","key":"9288_CR32","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1038\/nature20101","volume":"538","author":"A Graves","year":"2016","unstructured":"Graves A, Wayne G, Reynolds M, Harley T, Danihelka I, Grabska-Barwi\u0144ska A, Colmenarejo SG, Grefenstette E, Ramalho T et al (2016) Hybrid computing using a neural network with dynamic external memory. Nature 538(7626):471","journal-title":"Nature"},{"key":"9288_CR33","first-page":"6473","volume":"33","author":"J Guan","year":"2019","unstructured":"Guan J, Wang Y, Huang M (2019) Story ending generation with incremental encoding and commonsense knowledge. Proc AAAI Conf Artif Intell 33:6473\u20136480","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"5","key":"9288_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D (2018) A survey of methods for explaining black box models. ACM Comput Surv (CSUR) 51(5):1\u201342","journal-title":"ACM Comput Surv (CSUR)"},{"key":"9288_CR35","doi-asserted-by":"crossref","unstructured":"Guo S, Wang Q, Wang L, Wang B, Guo L (2018) Knowledge graph embedding with iterative guidance from soft rules. In: 32nd AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11918"},{"key":"9288_CR36","unstructured":"Harkous H, Fawaz K, Lebret R, Schaub F, Shin KG, Aberer K (2018) Polisis: automated analysis and presentation of privacy policies using deep learning. In: 27th {USENIX} security symposium ({USENIX} security 18), pp 531\u2013548"},{"key":"9288_CR37","unstructured":"Hill F, Bordes A, Chopra S, Weston J (2015) The Goldilocks principle: reading children\u2019s books with explicit memory representations. ArXiv preprint arXiv:151102301"},{"key":"9288_CR38","doi-asserted-by":"crossref","unstructured":"Hu Z, Ma X, Liu Z, Hovy E, Xing E (2016) Harnessing deep neural networks with logic rules. ArXiv preprint arXiv:160306318","DOI":"10.18653\/v1\/P16-1228"},{"key":"9288_CR39","doi-asserted-by":"crossref","unstructured":"Jobin A, Ienca M, Vayena E (2019) Artificial intelligence: the global landscape of ethics guidelines. ArXiv preprint arXiv:190611668","DOI":"10.1038\/s42256-019-0088-2"},{"issue":"10","key":"9288_CR40","doi-asserted-by":"publisher","first-page":"4065","DOI":"10.1016\/j.eswa.2013.01.001","volume":"40","author":"E Kontopoulos","year":"2013","unstructured":"Kontopoulos E, Berberidis C, Dergiades T, Bassiliades N (2013) Ontology-based sentiment analysis of twitter posts. Expert Syst Appl 40(10):4065\u20134074","journal-title":"Expert Syst Appl"},{"key":"9288_CR41","unstructured":"Kumar A, Irsoy O, Ondruska P, Iyyer M, Bradbury J, Gulrajani I, Zhong V, Paulus R, Socher R (2016) Ask me anything: dynamic memory networks for natural language processing. In: International conference on machine learning, pp 1378\u20131387"},{"key":"9288_CR42","unstructured":"Lagioia F, Ruggeri F, Drazewski K, Lippi M, Micklitz HW, Torroni P, Sartor G (2019) Deep learning for detecting and explaining unfairness in consumer contracts. In: Legal knowledge and information systems: JURIX 2019: the 32nd annual conference, frontiers in artificial intelligence and applications, vol 322. IOS Press, pp 43\u201352"},{"key":"9288_CR43","doi-asserted-by":"crossref","unstructured":"Lippi M, Lagioia F, Contissa G, Sartor G, Torroni P (2015) Claim detection in judgments of the eu court of justice. In: AI approaches to the complexity of legal systems. Springer, pp 513\u2013527","DOI":"10.1007\/978-3-030-00178-0_35"},{"issue":"4","key":"9288_CR44","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1038\/s42256-019-0042-3","volume":"1","author":"M Lippi","year":"2019","unstructured":"Lippi M, Contissa G, Lagioia F, Micklitz HW, Pa\u0142ka P, Sartor G, Torroni P (2019a) Consumer protection requires artificial intelligence. Nat Mach Intell 1(4):168\u2013169. https:\/\/doi.org\/10.1038\/s42256-019-0042-3","journal-title":"Nat Mach Intell"},{"issue":"2","key":"9288_CR45","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s10506-019-09243-2","volume":"27","author":"M Lippi","year":"2019","unstructured":"Lippi M, Pa\u0142ka P, Contissa G, Lagioia F, Micklitz HW, Sartor G, Torroni P (2019b) CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service. Artif Intell Law 27(2):117\u2013139","journal-title":"Artif Intell Law"},{"key":"9288_CR46","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1613\/jair.1.11519","volume":"67","author":"M Lippi","year":"2020","unstructured":"Lippi M, Contissa G, Jablonowska A, Lagioia F, Micklitz H, Palka P, Sartor G, Torroni P (2020) The force awakens: artificial intelligence for consumer law. J Artif Intell Res 67:169\u2013190. https:\/\/doi.org\/10.1613\/jair.1.11519","journal-title":"J Artif Intell Res"},{"issue":"1","key":"9288_CR47","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10603-015-9303-7","volume":"39","author":"M Loos","year":"2016","unstructured":"Loos M, Luzak J (2016) Wanted: a bigger stick. On unfair terms in consumer contracts with online service providers. J Consum Policy 39(1):63\u201390","journal-title":"J Consum Policy"},{"key":"9288_CR48","doi-asserted-by":"crossref","unstructured":"Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: 32nd AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.12048"},{"issue":"3","key":"9288_CR49","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s10603-017-9353-0","volume":"40","author":"HW Micklitz","year":"2017","unstructured":"Micklitz HW, Pa\u0142ka P, Panagis Y (2017) The empire strikes back: digital control of unfair terms of online services. J Consum Policy 40(3):367\u2013388","journal-title":"J Consum Policy"},{"key":"9288_CR50","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. ArXiv preprint arXiv:13013781"},{"key":"9288_CR51","doi-asserted-by":"crossref","unstructured":"Miller A, Fisch A, Dodge J, Karimi AH, Bordes A, Weston J (2016) Key-value memory networks for directly reading documents. ArXiv preprint arXiv:160603126","DOI":"10.18653\/v1\/D16-1147"},{"issue":"11","key":"9288_CR52","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller GA (1995) Wordnet: a lexical database for English. Commun ACM 38(11):39\u201341","journal-title":"Commun ACM"},{"key":"9288_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller T (2019) Explanation in artificial intelligence: insights from the social sciences. Artif Intell 267:1\u201338","journal-title":"Artif Intell"},{"key":"9288_CR54","doi-asserted-by":"crossref","unstructured":"Moens MF, Boiy E, Palau RM, Reed C (2007) Automatic detection of arguments in legal texts. In: Proceedings of the 11th international conference on artificial intelligence and law, pp 225\u2013230","DOI":"10.1145\/1276318.1276362"},{"key":"9288_CR55","unstructured":"Munkhdalai T, Sordoni A, Wang T, Trischler A (2019) Metalearned neural mem-ory. In: Wallach H, Larochelle H, Beygelzimer A, d'Alch\u2032e Buc F, Fox E, Gar-nett R (eds) Advances in neural information processing systems, Curran Associates, Inc., vol 32. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/182bd81ea25270b7d1c2fe8353d17fe6-Paper.pdf"},{"key":"9288_CR56","doi-asserted-by":"crossref","unstructured":"Obar JA, Oeldorf-Hirsch A (2016) The biggest lie on the internet: Ignoring the privacy policies and terms of service policies of social networking services. In: TPRC 44: The 44th research conference on communication, information and internet policy","DOI":"10.2139\/ssrn.2757465"},{"key":"9288_CR57","volume-title":"Weapons of math destruction: how big data increases inequality and threatens democracy","author":"C O\u2019Neil","year":"2016","unstructured":"O\u2019Neil C (2016) Weapons of math destruction: how big data increases inequality and threatens democracy. Crown Publishing Group, New York"},{"key":"9288_CR58","unstructured":"Prakash A, Zhao S, Hasan SA, Datla VV, Lee K, Qadir A, Liu J, Farri O (2016) Condensed memory networks for clinical diagnostic inferencing. CoRR arXiv:abs\/1612.01848"},{"key":"9288_CR59","doi-asserted-by":"crossref","unstructured":"Rockt\u00e4schel T, Bosnjak M, Singh S, Riedel S (2014) Low-dimensional embeddings of logic. In: Proceedings of the ACL 2014 workshop on semantic parsing, pp 45\u201349","DOI":"10.3115\/v1\/W14-2409"},{"key":"9288_CR60","doi-asserted-by":"crossref","unstructured":"Rockt\u00e4schel T, Singh S, Riedel S (2015) Injecting logical background knowledge into embeddings for relation extraction. In: Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1119\u20131129","DOI":"10.3115\/v1\/N15-1118"},{"key":"9288_CR61","unstructured":"Sadeh N, Acquisti A, Breaux TD, Cranor LF, McDonald AM, Reidenberg JR, Smith NA, Liu F, Russell NC, Schaub F, et al (2013) The usable privacy policy project: combining crowdsourcing. In: Machine learning and natural language processing to semi-automatically answer those privacy questions users care about Carnegie Mellon University technical report CMU-ISR-13-119, pp 1\u201324"},{"key":"9288_CR62","doi-asserted-by":"crossref","unstructured":"Schmunk S, H\u00f6pken W, Fuchs M, Lexhagen M (2013) Sentiment analysis: extracting decision-relevant knowledge from ugc. In: Information and communication technologies in tourism 2014. Springer, pp 253\u2013265","DOI":"10.1007\/978-3-319-03973-2_19"},{"key":"9288_CR63","first-page":"1085","volume":"87","author":"AD Selbst","year":"2018","unstructured":"Selbst AD, Barocas S (2018) The intuitive appeal of explainable machines. Fordham L Rev 87:1085","journal-title":"Fordham L Rev"},{"issue":"1","key":"9288_CR64","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10506-017-9197-6","volume":"25","author":"O Shulayeva","year":"2017","unstructured":"Shulayeva O, Siddharthan A, Wyner A (2017) Recognizing cited facts and principles in legal judgements. Artif Intell Law 25(1):107\u2013126","journal-title":"Artif Intell Law"},{"key":"9288_CR65","doi-asserted-by":"crossref","unstructured":"Speer R, Havasi C (2013) Conceptnet 5: a large semantic network for relational knowledge. In: The people\u2019s web meets NLP. Springer, pp 161\u2013176","DOI":"10.1007\/978-3-642-35085-6_6"},{"key":"9288_CR66","unstructured":"Sukhbaatar S, Weston J, Fergus R, et al (2015) End-to-end memory networks. In: Advances in neural information processing systems, pp 2440\u20132448"},{"key":"9288_CR67","doi-asserted-by":"crossref","unstructured":"Sun H, Dhingra B, Zaheer M, Mazaitis K, Salakhutdinov R, Cohen WW (2018) Open domain question answering using early fusion of knowledge bases and text. ArXiv preprint arXiv:180900782","DOI":"10.18653\/v1\/D18-1455"},{"key":"9288_CR68","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. ArXiv preprint arXiv:160508900","DOI":"10.18653\/v1\/D16-1021"},{"key":"9288_CR69","unstructured":"Torisawa K, et al (2007) Exploiting wikipedia as external knowledge for named entity recognition. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), pp 698\u2013707"},{"key":"9288_CR70","doi-asserted-by":"crossref","unstructured":"Wachter S, Mittelstadt B (2019) A right to reasonable inferences: re-thinking data protection law in the age of big data and ai. Colum Bus L Rev 494","DOI":"10.31228\/osf.io\/mu2kf"},{"key":"9288_CR71","doi-asserted-by":"crossref","unstructured":"Wang J, Wang Z, Zhang D, Yan J (2017) Combining knowledge with deep convolutional neural networks for short text classification. In: IJCAI, pp 2915\u20132921","DOI":"10.24963\/ijcai.2017\/406"},{"key":"9288_CR72","doi-asserted-by":"crossref","unstructured":"Wang WY, Mazaitis K, Cohen WW (2014) Structure learning via parameter learning. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management, pp 1199\u20131208","DOI":"10.1145\/2661829.2662022"},{"key":"9288_CR73","unstructured":"Weston J, Chopra S, Bordes A (2014) Memory networks. ArXiv preprint arXiv:14103916"},{"key":"9288_CR74","unstructured":"Xiong C, Merity S, Socher R (2016) Dynamic memory networks for visual and textual question answering. In: International conference on machine learning, pp 2397\u20132406"},{"key":"9288_CR75","unstructured":"Zaremba W, Sutskever I (2015) Reinforcement learning neural turing machines-revised. ArXiv preprint arXiv:150500521"},{"key":"9288_CR76","unstructured":"Zelikovitz S, Hirsh H (2003) Integrating background knowledge into text classification. In: IJCAI, pp 1448\u20131449"},{"key":"9288_CR77","doi-asserted-by":"crossref","unstructured":"Zhang J, Lertvittayakumjorn P, Guo Y (2019a) Integrating semantic knowledge to tackle zero-shot text classification. ArXiv preprint arXiv:190312626","DOI":"10.18653\/v1\/N19-1108"},{"key":"9288_CR78","doi-asserted-by":"crossref","unstructured":"Zhang Z, Han X, Liu Z, Jiang X, Sun M, Liu Q (2019b) Ernie: Enhanced language representation with informative entities. ArXiv preprint arXiv:190507129","DOI":"10.18653\/v1\/P19-1139"},{"key":"9288_CR79","doi-asserted-by":"crossref","unstructured":"Zhou H, Young T, Huang M, Zhao H, Xu J, Zhu X (2018) Commonsense knowledge aware conversation generation with graph attention. In: IJCAI, pp 4623\u20134629","DOI":"10.24963\/ijcai.2018\/643"}],"container-title":["Artificial Intelligence and Law"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10506-021-09288-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10506-021-09288-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10506-021-09288-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T01:18:11Z","timestamp":1672103891000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10506-021-09288-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,11]]},"references-count":79,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["9288"],"URL":"https:\/\/doi.org\/10.1007\/s10506-021-09288-2","relation":{},"ISSN":["0924-8463","1572-8382"],"issn-type":[{"value":"0924-8463","type":"print"},{"value":"1572-8382","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,11]]},"assertion":[{"value":"29 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}