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Künstliche Intelligenz in Wirtschaft & Gesellschaft pp 275–295Cite as

Die Anwendung von Machine Learning zur Gewinnung von Erkenntnissen aus Dokumentenstapeln

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Part of the FOM-Edition book series (FOMEDITION)

Zusammenfassung

„Document Understanding“ ist das tiefe Verständnis eines Textes. Im Kern geht es um die Konvertierung von unstrukturierten Daten in Informationen und für Unternehmen gleichermaßen um die die Einhaltung von Governance- und Compliance-Richtlinien. Zum Einsatz kommt zumeist eine Sammlung von verschiedenen Methoden, zu denen unter anderem die Document Classification oder auch die Entity Extraction gehören. Viele Ansätze beruhen auf regelbasierten Systemen respektive auf statistischen Verfahren.

Der Einsatz von Machine Learning zur massenhaften Erschließung unstrukturierter Dokumente eröffnet neue Wege, um unter anderem Beziehungen zwischen Dokumenten sichtbar zu machen. ML ermöglicht Vorhersagen zur Dokumentenklassifizierung oder etwa die Extraktion von Wissen aus Textpassagen, Grafiken oder Feldern jenseits einfacher Mustererkennung. ML stellt Möglichkeiten einer semantischen Suche über Dokumente hinweg zur Verfügung und legt den Grundstein für erweiterte Analysen beispielsweise der Anomalieerkennung.

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Notes

  1. 1.

    Kurzbezeichnung für einen Trainingsdatensatz bereitgestellt durch die Message Understanding Conferences.

  2. 2.

    https://www.tensorflow.org/tutorials/keras/text_classification_with_hub

  3. 3.

    Vielfach erfolgt eine Transkription für Audio- und Video-Daten durch das entsprechende System automatisch.

  4. 4.

    https://github.com/cayleygraph/cayley

  5. 5.

    https://patents.google.com/patent/US20170011116A1/en

Literatur

  • Aiello, M., Monz, C., Todoran, L., & Worring, M. (2003a). Document understanding for a broad class of documents. Journal on Document Analysis and Recognition, 5, 1–16. https://doi.org/10.1007/s10032-002-0080-x.

    CrossRef  Google Scholar 

  • Aiello, M., Monz, C., Todoran, L., & Worring, M. (2003b). Document understanding for a broad class of documents. Journal on Document Analysis and Recognition. https://doi.org/10.1007/s10032-002-0080-x.

    CrossRef  Google Scholar 

  • Basari, A. S. H., Hussin, B., Ananta, I. G. P., & Zeniarja, J. (2013). Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Engineering, 53, 453–462. https://doi.org/10.1016/j.proeng.2013.02.059.

    CrossRef  Google Scholar 

  • Buchkremer, R., Demund, A., Ebener, S., et al. (2019). The application of artificial intelligence technologies as a substitute for reading and to support and enhance the authoring of scientific review articles. IEEE Access, 7, 65263–65276. https://doi.org/10.1109/ACCESS.2019.2917719.

    CrossRef  Google Scholar 

  • Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28, 15–21.

    CrossRef  Google Scholar 

  • Cash, G. L., & Hatamian, M. (1987). Optical character recognition by the method of moments. Comput Vision, Graph Image Process, 39, 291–310.

    CrossRef  Google Scholar 

  • Chinchor, N., & Robinson, P. (1997). MUC-7 named entity task definition. Proceedings of the Sixth Message Understanding Conference (MUC-6), 21.

    Google Scholar 

  • Cimiano, P., & Völker, J. (2005). Towards large-scale, open-domain and ontology-based named entity classification. International Conference Recent Advances in Natural Language Process RANLP, 2005(1), 166–172.

    Google Scholar 

  • Dai, W., Yang, Q., Xue, G. R., & Yu, Y. (2007). Boosting for transfer learning. International Conference Proceedings Series, 227, 193–200. https://doi.org/10.1145/1273496.1273521.

    CrossRef  Google Scholar 

  • Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis BT – Tech Mining: Exploiting Science and Technology Information Resources. Technological Forecasting and Social Change, 73, 981–1012. https://doi.org/10.1016/j.techfore.2006.04.004.

    CrossRef  Google Scholar 

  • Dang, H. T. (2005). Overview of DUC 2005. In Proceedings of the document understanding conference.

    Google Scholar 

  • Deng, L. (2012). The MNIST database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29, 141–142. https://doi.org/10.1109/MSP.2012.2211477.

    CrossRef  Google Scholar 

  • Dengel, A., & Dubiel, F. (1995). Clustering and classification of document structure-a machine learning approach. In Proceedings of 3rd international conference on document analysis and recognition, 587–591.

    Google Scholar 

  • Dengel, A. R. (2003). Making documents work: Challenges for document understanding. In Proceedings of the international conference on document analysis and recognition, ICDAR.

    Google Scholar 

  • Furman, B. L., & Wilson, G. A. (1978). Effects upon plasma glucose of inhibitors of 5Ht uptake and their interaction with 5???Hydroxytryptophan in producing hypoglycaemia. Journal of Pharmacy and Pharmacology, 30, 53P–53P. https://doi.org/10.1111/j.2042-7158.1978.tb10760.x.

    CrossRef  Google Scholar 

  • Gharehchopogh, F. S., & Khalifelu, Z. A. (2011). Analysis and evaluation of unstructured data: Text mining versus natural language processing. In 5th International Conference on Application of Information and Communication Technologies (AICT), 1–4.

    Google Scholar 

  • Glorot, X., Bordes, A., & Bengio, Y. (2011). Domain adaptation for large-scale sentiment classification: A deep learning approach. Proceedings of the 28th International Conference on Machine Learning ICML, 2011, 513–520.

    Google Scholar 

  • Google Patents. (o. J.). Smart-home automation system that suggests or autmatically implements selected household policies based on sensed observations. Zugegriffen: 23. Dez. 2019.

    Google Scholar 

  • Gray, J., & Rumpe, B. (2017). Models for the digital transformation. Software & Systems Modeling, 16, 307–308. https://doi.org/10.1007/s10270-017-0596-7.

    CrossRef  Google Scholar 

  • Guerra, P. H. C., Veloso, A., Meira, W., & Almeida, V. (2011). From bias to opinion: A transfer-learning approach to real-time sentiment analysis. Processding ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 150–158. https://doi.org/10.1145/2020408.2020438.

  • Gunning, D. (2017). Explainable artificial intelligence (xai). The Defense Advanced Research Projects Agency (DARPA), nd Web 2.

    Google Scholar 

  • Guo, J., Xu, G., Cheng, X., & Li, H. (2009). Named entity recognition in query. Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, 2009, 267–274. https://doi.org/10.1145/1571941.1571989.

    CrossRef  Google Scholar 

  • Hamdaqa, M., & Hamou-Lhadj, A. (2009). Citation analysis: An approach for facilitating the understanding and the analysis of regulatory compliance documents. ITNG 2009 – 6th International Conference on Information Technology- New Generations, 2009, 278–283. https://doi.org/10.1109/ITNG.2009.161.

  • Han, A. L.-F., Wong, D. F., & Chao, L. S. (2013). Chinese named entity recognition with conditional random fields in the light of Chinese characteristics BT – Language processing. In M. A. Kłopotek, J. Koronacki, M. Marciniak, et al. (Hrsg.), Intelligent information systems (S. 57–68). Berlin: Springer.

    Google Scholar 

  • Handley, J. C., Namboodiri, A. M., & Zanibbi, R. (2005). Document understanding system using stochastic context-free grammars. International Conference on Document Analysis and Recognition, ICDAR, 2005, 511–515. https://doi.org/10.1109/ICDAR.2005.93.

    CrossRef  Google Scholar 

  • Hardy, H., Shimizu, N., Strzalkowski, T., et al. (2002). Cross-document summarization by concept classification. SIGIR Forum (ACM Spec Interes Gr Inf Retrieval), 2002, 121–128.

    Google Scholar 

  • Hasan, S., O’Riain, S., & Curry, E. (2012). Approximate semantic matching of heterogeneous events. Proceeding of the 6th ACM International Conference on Distributed Event-based system DEBS’, 12, 252–263. https://doi.org/10.1145/2335484.2335512.

  • Holzinger, A. (2018). From machine learning to explainable AI. In 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), S. 55–66.

    Google Scholar 

  • Howard, J., & Ruder, S. (2018). Universal language model fine-tuning for text classification. ACL 2018 – 56th Annual Meeting of the Association for Computational Linguistics Proceeding Conference, 1, 328–339.

    Google Scholar 

  • Ikonomakis, M., Kotsiantis, S., & Tampakas, V. (2005). Text classification using machine learning techniques. WSEAS Transactions on Computers, 4, 966–974.

    Google Scholar 

  • Jindal, R., Malhotra, R., & Jain, A. (2015). Techniques for text classification: Literature review and current trends. Webology, 12, 1–28.

    Google Scholar 

  • Kapetanios, E., & Doina Tatar, C. S. (2013). Natural language processing: Semantic aspects (S. 346). https://doi.org/10.1201/b15472.

  • Khan, A., Baharudin, B., Lee, L. H., & Khan, K. (2010). A review of machine learning algorithms for text-documents classification. Journal of Advanced Information Technology, 1, 4–20.

    CrossRef  Google Scholar 

  • Kiyani, F., & Tas, O. (2017). A survey automatic text summarization. Pressacademia, 5, 205–213. https://doi.org/10.17261/pressacademia.2017.591.

  • Lanjouw, J. O., Pakes, A., & Putnam, J. (1998). How to count patents and value intellectual property: The uses of patent renewal and application data. The Journal of Industrial Economics, 46, 405–432.

    CrossRef  Google Scholar 

  • Lee, L. S., & Chen, B. (2005). Spoken document understanding and organization. IEEE Signal Processing Magazine, 22(5), 42–60. https://doi.org/10.1109/MSP.2005.1511823.

    CrossRef  Google Scholar 

  • Lin, D., & Wu, X. (2009). Phrase clustering for discriminative learning. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2, 1030–1038. https://doi.org/10.3115/1690219.1690290.

  • Lin, Y., Liu, Z., Sun, M., et al. (2015). Learning entity and relation embeddings for knowledge graph completion. Proceeding of National Conference on Artificial Intelligence, 3, 2181–2187.

    Google Scholar 

  • Liu, T., Chen, Z., Zhang, B., et al. (2004). Improving text classification using local latent semantic indexing. Proceeding – Fourth IEEE International Conference on Data Mining, ICDM, 2004, 162–169.

    Google Scholar 

  • Marinai, S., & Fujisawa, H. (2008). Machine learning in document analysis and recognition. Heidelberg: Springer.

    Google Scholar 

  • Mooney, R. J., & Roy, L. (2000). Content-based book recommending using learning for text categorization. Proceeding of ACM International Journal on Digital Libraries, 2000, 195–204.

    Google Scholar 

  • Mori, S., Nishida, H., & Yamada, H. (1999). Optical character recognition (1. Aufl.). New York: Wiley.

    Google Scholar 

  • Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes, 30, 3–26.

    CrossRef  Google Scholar 

  • Nigyogi, D., & Srihari, S. N. (1986). A rule-based system for document understanding. Proceeding of AAAI, 1986, 789–793.

    Google Scholar 

  • Nrl EM, Nrl DP, & Nyu RG. (1998). MUC-7 EVALUATION OF IE TECHNOLOGY : Overview of Results MUC-7 Program Committee. Program.

    Google Scholar 

  • Olivas, E. S., Guerrero, J. D. M., Martinez, S. M., et al. (2009). Handbook of research on machine learning applications and trends: Algorithms, methods, and techniques (S. 1–703). https://doi.org/10.4018/978-1-60566-766-9.

  • Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. LREc, 2010, 320–1326.

    Google Scholar 

  • Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359.

    CrossRef  Google Scholar 

  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.

    Google Scholar 

  • Passonneau, R. (2011). Sentiment analysis of twitter data. Proceeding of Work Language Social Media (LSM 2011), 2011, 30–38.

    Google Scholar 

  • Pinto, D., Gómez-Adorno, H., Vilariño, D., & Singh, V. K. (2014). A graph-based multi-level linguistic representation for document understanding. Pattern recognition letters. https://doi.org/10.1016/j.patrec.2013.12.004.

    CrossRef  Google Scholar 

  • Prince, V, & Labadié, A. (2007). Text segmentation based on document understanding for information retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    Google Scholar 

  • Ratinov, L., & Roth, D. (2009). Design challenges and misconceptions in named entity recognition. CoNLL 2009 – Proceedings of Thirteen Conference on Computational Natural Language Learning, 2009, 147–155.

    Google Scholar 

  • Samek W., Wiegand T., & Müller K.-R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. ITU Journal: ICT Discoveries, Special Issue No. 1 – Impact Artificial intelligence (AI) Communication Network Service, 1(1), 39–48.

    Google Scholar 

  • Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34, 1–47. https://doi.org/10.1145/505282.505283.

    CrossRef  Google Scholar 

  • Shaalan, K., & Raza, H. (2008). Arabic named entity recognition from diverse text types. In International Conference on Natural Language Processing, S. 440–451.

    Google Scholar 

  • Stack, K. P. (1998). Competitive intelligence. Intelligence and National Security, 13, 194–202. https://doi.org/10.1080/02684529808432511.

    CrossRef  Google Scholar 

  • Stevenson, R. A., Mikels, J. A., & James, T. W. (2007). Characterization of the affective norms for english words by discrete emotional categories. Behavior Research Methods, 39, 1020–1024.

    CrossRef  Google Scholar 

  • Tanner, S. (2004). Deciding whether optical character recognition is feasible. London: King’s Digital Consultancy Services, 1–11.

    Google Scholar 

  • Taylor, S. L., Lipshutz, M., Dahl, D. A., &Weir, C. (1993). An intelligent document understanding system. In Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR ’93), S. 107–110.

    Google Scholar 

  • Tramèr, F., Zhang, F., Juels, A., et al. (2016). Stealing machine learning models via prediction apis. In 25th ${$USENIX$}$ Security Symposium (${$USENIX$}$ Security 16), S. 601–618.

    Google Scholar 

  • Vincent, L. (2007). Google book search: Document understanding on a massive scale. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

    Google Scholar 

  • Wang, K., Babenko, B., & Belongie, S. (2011). End-to-end scene text recognition. Proceedings of the IEEE International Conference on Computer Vision, 2011, 1457–1464. https://doi.org/10.1109/ICCV.2011.6126402.

  • Yoshida, Y., Hirao, T., Iwata, T., et al. (2011). Transfer learning for multiple-domain sentiment analysis – Identifying domain dependent/independent word polarity. Proceeding of the National Conference on Artificial Intelligence, 2, 1286–1291.

    Google Scholar 

  • Yu, B., & Xu, Z. ben. (2008). A comparative study for content-based dynamic spam classification using four machine learning algorithms. Knowledge-Based Syst, 21, 355–362. https://doi.org/10.1016/j.knosys.2008.01.001.

    CrossRef  Google Scholar 

  • Yuan, Y., & Zhou, Y. (2013). Twitter Sentiment Analysis with Recursive Neural Networks. CS224D Course Projects, 2013, 1–8.

    Google Scholar 

  • Zhai, H., Lingren, T., Deleger, L., et al. (2013). Web 2.0-based crowdsourcing for high-quality gold standard development in clinical natural language processing. Journal of Medical Internet Research, 15. https://doi.org/10.2196/jmir.2426.

  • Zweig, G. G., & Padmanabhan, M. (2005). Information extraction from documents with regular expression matching. Washington: U.S. Patent and Trademark Office.

    Google Scholar 

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  1. FOM Hochschule, Köln, Deutschland

    Stefan Ebener

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  1. Stefan Ebener
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Correspondence to Stefan Ebener .

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  1. FOM Hochschule für Oekonomie & Management, Düsseldorf, Germany

    Prof. Dr. Rüdiger Buchkremer

  2. FOM Hochschule für Oekonomie & Management, Essen, Germany

    Prof. Dr. Thomas Heupel

  3. FOM Hochschule für Oekonomie & Management, Frankfurt a. M., Germany

    Prof. Dr. Oliver Koch

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Ebener, S. (2020). Die Anwendung von Machine Learning zur Gewinnung von Erkenntnissen aus Dokumentenstapeln. In: Buchkremer, R., Heupel, T., Koch, O. (eds) Künstliche Intelligenz in Wirtschaft & Gesellschaft. FOM-Edition. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-29550-9_15

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