{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:47:51Z","timestamp":1776077271203,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007515","name":"Universidad de Valladolid","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007515","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Artificial intelligence methods based on deep learning (DL) have recently made significant progress in many different areas including free text classification and sentiment analysis. We believe that corporate governance is one of these areas, where DL can generate very valuable and differential knowledge, for example, by analyzing the biographies of independent directors, which allows for qualitative modeling of their profile in an automatic way.  For this technology to be accepted it is important to be able to explain how it generates its results. In this work we have developed a six-dimensional labeled dataset of independent director biographies, implemented three recurrent DL models based on LSTM and transformers along with four ensembles, one of which is an innovative proposal based on a multi-layer perceptron (MLP), trained them using Spanish language and economics and finance terminology and performed a comprehensive test study that demonstrates the accuracy of the results. We have also performed a complete study of explainability using the SHAP methodology by comparatively analyzing the developed models. We have achieved a mean error (MAE) of 8% in the modeling of the open text biographies, which has allowed us to perform a case study of time analysis that has detected significant variations in the composition of the Standard Expertise Profile (SEP) of the boards of directors, related to the crisis of the period 2008\u20132013. This work shows that DL technology can be accurately applied to free text analysis in the finance and economic domain, by automatically analyzing large volumes of data to generate knowledge that would have been unattainable by other means.<\/jats:p>","DOI":"10.1007\/s00521-024-09474-8","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T16:02:11Z","timestamp":1708963331000},"page":"7509-7527","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Interpretability of deep learning models in analysis of Spanish financial text"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0453-4387","authenticated-orcid":false,"given":"C\u00e9sar","family":"Vaca","sequence":"first","affiliation":[]},{"given":"Manuel","family":"Astorgano","sequence":"additional","affiliation":[]},{"given":"Alfonso J.","family":"L\u00f3pez-Rivero","sequence":"additional","affiliation":[]},{"given":"Fernando","family":"Tejerina","sequence":"additional","affiliation":[]},{"given":"Benjam\u00edn","family":"Sahelices","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"9474_CR1","doi-asserted-by":"publisher","unstructured":"Conneau A, Schwenk H, Cun YL, Barrault L (2017) Very deep convolutional networks for text classification. In: Proceedings of 15th conference of the European chapter of the association for computational linguistics 2. https:\/\/doi.org\/10.18653\/v1\/e17-1104","DOI":"10.18653\/v1\/e17-1104"},{"key":"9474_CR2","doi-asserted-by":"crossref","unstructured":"Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. arXiv:1801.06146","DOI":"10.18653\/v1\/P18-1031"},{"key":"9474_CR3","doi-asserted-by":"publisher","first-page":"65","DOI":"10.4018\/JDM.2021100105","volume":"32","author":"S Lyu","year":"2021","unstructured":"Lyu S, Liu J (2021) Convolutional recurrent neural networks for text classification. J Datab Manag 32:65\u201382. https:\/\/doi.org\/10.4018\/JDM.2021100105","journal-title":"J Datab Manag"},{"key":"9474_CR4","unstructured":"Radford A, Jozefowicz R, Sutskever I (2017) Learning to generate reviews and discovering sentiment. arXiv:1704.01444"},{"key":"9474_CR5","doi-asserted-by":"crossref","unstructured":"Mnassri K, Rajapaksha P, Farahbakhsh R, Crespi N (2022) BERT-based ensemble approaches for hate speech detection. arXiv:2209.06505","DOI":"10.1109\/GLOBECOM48099.2022.10001325"},{"key":"9474_CR6","doi-asserted-by":"publisher","first-page":"60","DOI":"10.9781\/ijimai.2022.09.005","volume":"7","author":"C Vaca","year":"2022","unstructured":"Vaca C, Tejerina F, Sahelices B (2022) Board of directors\u2019 profile: a case for deep learning as a valid methodology to finance research. Int J Interact Multimedia Artif Intell 7:60. https:\/\/doi.org\/10.9781\/ijimai.2022.09.005","journal-title":"Int J Interact Multimedia Artif Intell"},{"key":"9474_CR7","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. Naacl-Hlt https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"9474_CR8","unstructured":"Merity S, Keskar NS, Socher R (2018) Regularizing and optimizing LSTM language models. https:\/\/openreview.net\/forum?id=SyyGPP0TZ"},{"key":"9474_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05681-1","author":"KP Nkabiti","year":"2021","unstructured":"Nkabiti KP, Chen Y (2021) Application of solely self-attention mechanism in CSI-fingerprinting-based indoor localization. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05681-1","journal-title":"Neural Comput Appl"},{"key":"9474_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06409-5","author":"D Xia","year":"2022","unstructured":"Xia D, Yang N, Jiang S, Hu Y, Li Y, Li H, Wang L (2022) A parallel NAW-DBLSTM algorithm on spark for traffic flow forecasting. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06409-5","journal-title":"Neural Comput Appl"},{"key":"9474_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07403-1","author":"F Colasanto","year":"2022","unstructured":"Colasanto F, Grilli L, Santoro D, Villani G (2022) BERT\u2019s sentiment score for portfolio optimization: a fine-tuned view in Black and Litterman model. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-022-07403-1","journal-title":"Neural Comput Appl"},{"key":"9474_CR12","unstructured":"Lundberg S, Lee S-I (2017) A unified approach to interpreting model predictions. arXiv:1705.07874"},{"key":"9474_CR13","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, \u0141ukasz Kaiser, Polosukhin I (2017) Attention is all you need. arXiv:1706.03762"},{"key":"9474_CR14","doi-asserted-by":"publisher","first-page":"129176","DOI":"10.1109\/ACCESS.2022.3223049","volume":"10","author":"M Rizwan","year":"2022","unstructured":"Rizwan M, Mushtaq MF, Akram U, Mehmood A, Ashraf I, Sahelices B (2022) Depression classification from tweets using small deep transfer learning language models. IEEE Access 10:129176\u2013129189. https:\/\/doi.org\/10.1109\/ACCESS.2022.3223049","journal-title":"IEEE Access"},{"key":"9474_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2018.00001","author":"P Giudici","year":"2018","unstructured":"Giudici P (2018) Fintech risk management: a research challenge for artificial intelligence in finance. Front Artif Intell. https:\/\/doi.org\/10.3389\/frai.2018.00001","journal-title":"Front Artif Intell"},{"key":"9474_CR16","unstructured":"Tadapaneni NR (2020) Artificial intelligence in finance and investment. Int J Innov Res Sci Eng Technol (IJIRSET) 9(5)"},{"key":"9474_CR17","doi-asserted-by":"publisher","first-page":"3265","DOI":"10.1093\/rfs\/hhaa079","volume":"34","author":"K Li","year":"2021","unstructured":"Li K, Mai F, Shen R, Yan X (2021) Measuring corporate culture using machine learning. Rev Financ Stud 34:3265\u20133315. https:\/\/doi.org\/10.1093\/rfs\/hhaa079","journal-title":"Rev Financ Stud"},{"key":"9474_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3027314","author":"E Tjoa","year":"2021","unstructured":"Tjoa E, Guan C (2021) A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2020.3027314","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9474_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-08987-4","author":"S Lapuschkin","year":"2019","unstructured":"Lapuschkin S, W\u00e4ldchen S, Binder A, Montavon G, Samek W, M\u00fcller KR (2019) Unmasking clever Hans predictors and assessing what machines really learn. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-019-08987-4","journal-title":"Nat Commun"},{"key":"9474_CR20","doi-asserted-by":"publisher","unstructured":"Peters ME, Neumann M, Zettlemoyer L, Yih WT (2018) Dissecting contextual word embeddings: architecture and representation. https:\/\/doi.org\/10.18653\/v1\/d18-1179","DOI":"10.18653\/v1\/d18-1179"},{"key":"9474_CR21","unstructured":"Canete J, Chaperon G, Fuentes R, Ho J-H, Kang H, P\u00e9rez J (2020) Spanish pre-trained BERT model and evaluation data. Pml4dc at iclr 2020, 1\u201310"},{"key":"9474_CR22","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized BERT pretraining approach. arXiv:1907.11692"},{"key":"9474_CR23","unstructured":"OpenAI: ChatGPT (2023). https:\/\/openai.com\/blog\/chatgpt"},{"key":"9474_CR24","unstructured":"OpenAI: GPT-4 Technical Report (2023)"},{"key":"9474_CR25","unstructured":"Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, Bashlykov N, Batra S, Bhargava P, Bhosale S, Bikel D, Blecher L, Ferrer CC, Chen M, Cucurull G, Esiobu D, Fernandes J, Fu J, Fu W, Fuller B, Gao C, Goswami V, Goyal N, Hartshorn A, Hosseini S, Hou R, Inan H, Kardas M, Kerkez V, Khabsa M, Kloumann I, Korenev A, Koura PS, Lachaux M-A, Lavril T, Lee J, Liskovich D, Lu Y, Mao Y, Martinet X, Mihaylov T, Mishra P, Molybog I, Nie Y, Poulton A, Reizenstein J, Rungta R, Saladi K, Schelten A, Silva R, Smith EM, Subramanian R, Tan XE, Tang B, Taylor R, Williams A, Kuan JX, Xu P, Yan Z, Zarov I, Zhang Y, Fan A, Kambadur M, Narang S, Rodriguez A, Stojnic R, Edunov S, Scialom T (2023) Llama 2: open foundation and fine-tuned chat models"},{"key":"9474_CR26","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \"Why should i trust you?\" explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"issue":"1","key":"9474_CR27","doi-asserted-by":"publisher","first-page":"7","DOI":"10.2139\/ssrn.233111","volume":"9","author":"BE Hermalin","year":"2003","unstructured":"Hermalin BE, Weisbach MS (2003) Boards of directors as an endogenously determined institution: a survey of the economic literature. FRNBY Policy Rev 9(1):7\u201326. https:\/\/doi.org\/10.2139\/ssrn.233111","journal-title":"FRNBY Policy Rev"},{"key":"9474_CR28","unstructured":"CNMV: C\u00f3digo de buen gobierno de las sociedades cotizadas (2015). https:\/\/www.cnmv.es\/DocPortal\/Publicaciones\/CodigoGov\/CBG_2020.pdf"},{"key":"9474_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2007.05.009","author":"B G\u00fcner","year":"2018","unstructured":"G\u00fcner B, Malmendier U, Tate G (2018) Financial expertise of directors. J Financ Econ. https:\/\/doi.org\/10.1016\/j.jfineco.2007.05.009","journal-title":"J Financ Econ"},{"key":"9474_CR30","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/S0929-1199(99)00004-8","volume":"5","author":"JR Booth","year":"1999","unstructured":"Booth JR, Deli DN (1999) On executives of financial institutions as outside directors. J Corp Finance 5:227\u2013250. https:\/\/doi.org\/10.1016\/S0929-1199(99)00004-8","journal-title":"J Corp Finance"},{"key":"9474_CR31","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s11156-017-0635-z","volume":"50","author":"O Faleye","year":"2018","unstructured":"Faleye O, Hoitash R, Hoitash U (2018) Industry expertise on corporate boards. Rev Quant Financ Acc 50:441\u20132479. https:\/\/doi.org\/10.1007\/s11156-017-0635-z","journal-title":"Rev Quant Financ Acc"},{"key":"9474_CR32","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1086\/431448","volume":"78","author":"EM Fich","year":"2005","unstructured":"Fich EM (2005) Are some outside directors better than others? Evidence from director appointments by fortune 1000 firms. J Bus 78:1943\u20131972. https:\/\/doi.org\/10.1086\/431448","journal-title":"J Bus"},{"key":"9474_CR33","doi-asserted-by":"publisher","first-page":"6100","DOI":"10.1080\/00036846.2018.1489501","volume":"50","author":"Z Qiao","year":"2018","unstructured":"Qiao Z, Chen KY, Hung S (2018) Professionals inside the board room: accounting expertise of directors and dividend policy. Appl Econ 50:6100\u20136111. https:\/\/doi.org\/10.1080\/00036846.2018.1489501","journal-title":"Appl Econ"},{"key":"9474_CR34","doi-asserted-by":"publisher","first-page":"2099","DOI":"10.2308\/accr-10135","volume":"86","author":"J Krishnan","year":"2011","unstructured":"Krishnan J, Wen Y, Zhao W (2011) Legal expertise on corporate audit committees and financial reporting quality. Account Rev 86:2099\u20132130. https:\/\/doi.org\/10.2308\/accr-10135","journal-title":"Account Rev"},{"key":"9474_CR35","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1111\/1475-679X.12038","volume":"52","author":"JF Houston","year":"2014","unstructured":"Houston JF, Jiang L, Lin C, Ma Y (2014) Political connections and the cost of bank loans. J Account Res 52:193\u2013243. https:\/\/doi.org\/10.1111\/1475-679X.12038","journal-title":"J Account Res"},{"key":"9474_CR36","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1086\/320271","volume":"44","author":"A Agrawal","year":"2001","unstructured":"Agrawal A, Knoeber CR (2001) Do some outside directors play a political role? J Law Econ 44:179\u2013198. https:\/\/doi.org\/10.1086\/320271","journal-title":"J Law Econ"},{"key":"9474_CR37","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3954290","author":"Y Xie","year":"2021","unstructured":"Xie Y, Xu J, Zhu R (2021) Academic directors and corporate innovation. SSRN Pap. https:\/\/doi.org\/10.2139\/ssrn.3954290","journal-title":"SSRN Pap"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09474-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09474-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09474-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T13:20:42Z","timestamp":1711027242000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09474-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":37,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9474"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09474-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]},"assertion":[{"value":"10 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}