Gotowe bibliografie tematyczne / Information Extraction

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  1. Artykuły w czasopismach
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Gotowa bibliografia na temat „Information Extraction”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Information Extraction"

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Zdravcheva, Neli. "INFORMATION EXTRACTION FROM MULTISPECTRAL SATELLITE IMAGES." Journal Scientific and Applied Research 24, no. 1 (2023): 25–31. http://dx.doi.org/10.46687/jsar.v24i1.364.

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The article analyzes various methods and approaches of modern remote sensing that can be used in the processing of multispectral satellite images in order to effectively extract visual information about territories for which preliminary data is not available. Attention is paid to the creation of new derivative images (synthesized and indexed) and to performing pixel-oriented computer non supervised classification. A series of experiments have been made that clearly reveal the advantages and conveniences of remote retrieval of information from multispectral satellite images in a territory for w
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Sarawagi, Sunita. "Information Extraction." Foundations and Trends® in Databases 1, no. 3 (2007): 261–377. http://dx.doi.org/10.1561/1900000003.

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Cowie, Jim, and Wendy Lehnert. "Information extraction." Communications of the ACM 39, no. 1 (1996): 80–91. http://dx.doi.org/10.1145/234173.234209.

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McCallum, Andrew. "Information Extraction." Queue 3, no. 9 (2005): 48–57. http://dx.doi.org/10.1145/1105664.1105679.

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Grishman, Ralph. "Information Extraction." IEEE Intelligent Systems 30, no. 5 (2015): 8–15. http://dx.doi.org/10.1109/mis.2015.68.

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Kassaie, Besat, and Frank Wm Tompa. "Autonomously Computable Information Extraction." Proceedings of the VLDB Endowment 16, no. 10 (2023): 2431–43. http://dx.doi.org/10.14778/3603581.3603585.

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Most optimization techniques deployed in information extraction systems assume that source documents are static. Instead, extracted relations can be considered to be materialized views defined by a language built on regular expressions. Using this perspective, we can provide an efficient verifier (using static analysis) that can be used to avoid the high cost of re-extracting information after an update. In particular, we propose an efficient mechanism to identify updates for which we can autonomously compute an extracted relation. We present experimental results that support the feasibility a
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Gao, Zhangchi, and Shoubin Li. "Joint Information Extraction Model Based on Feature Sharing." International Journal of Emerging Technologies and Advanced Applications 1, no. 2 (2024): 16–18. http://dx.doi.org/10.62677/ijetaa.2402107.

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To address the challenge of efficiently and accurately extracting entities, relationships, and events from unstructured text, a joint information extraction model based on feature sharing is proposed. This model utilizes the contextual information of entities, relationships, and events, and integrates entity extraction, relationship extraction, and event extraction tasks through a multi-feature cascade encoder to achieve joint extraction. To validate the effectiveness of the model, comparative analysis was conducted on military news datasets, comparing against two typical information extractio
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Ling, Xiao, and Daniel Weld. "Temporal Information Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1385–90. http://dx.doi.org/10.1609/aaai.v24i1.7512.

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Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1/20/93 — 1/20/01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible.
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Turmo, Jordi, Alicia Ageno, and Neus Català. "Adaptive information extraction." ACM Computing Surveys 38, no. 2 (2006): 4. http://dx.doi.org/10.1145/1132956.1132957.

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Vo, Duc-Thuan, and Ebrahim Bagheri. "Open information extraction." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630003. http://dx.doi.org/10.1142/s2425038416300032.

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Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation typ
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Rozprawy doktorskie na temat "Information Extraction"

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Labský, Martin. "Information Extraction from Websites using Extraction Ontologies." Doctoral thesis, Vysoká škola ekonomická v Praze, 2002. http://www.nusl.cz/ntk/nusl-77102.

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Automatic information extraction (IE) from various types of text became very popular during the last decade. Owing to information overload, there are many practical applications that can utilize semantically labelled data extracted from textual sources like the Internet, emails, intranet documents and even conventional sources like newspaper and magazines. Applications of IE exist in many areas of computer science: information retrieval systems, question answering or website quality assessment. This work focuses on developing IE methods and tools that are particularly suited to extraction from
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Arpteg, Anders. "Intelligent semi-structured information extraction : a user-driven approach to information extraction /." Linköping : Dept. of Computer and Information Science, Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/tek946s.pdf.

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Swampillai, Kumutha. "Information extraction across sentences." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575468.

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Most relation extraction systems identify relations by searching within- sentences (within-sentence relations). Such an approach excludes finding any relations that cross sentence boundaries (cross-sentence relations). This thesis quantifies the cross-sentence relations in two major information ex- traction corpora: ACE03 (9.4%) and MUC6 (27.4%), revealing the extent of this limitation. In response. a composite kernel approach to cross-sentence relation extraction is proposed which models relations using parse tree and fiat surface features. Support vector machine classifiers are trained using
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Tablan, Mihai Valentin. "Toward portable information extraction." Thesis, University of Sheffield, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522379.

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Leen, Gayle. "Context assisted information extraction." Thesis, University of the West of Scotland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446043.

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Sottovia, Paolo. "Information Extraction from data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242992.

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Data analysis is the process of inspecting, cleaning, extract, and modeling data with the intention of extracting useful information in order to support users in their decisions. With the advent of Big Data, data analysis was becoming more complicated due to the volume and variety of data. This process begins with the acquisition of the data and the selection of the data that is useful for the desiderata analysis. With such amount of data, also expert users are not able to inspect the data and understand if a dataset is suitable or not for their purposes. In this dissertation, we focus on five
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Sottovia, Paolo. "Information Extraction from data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242992.

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Data analysis is the process of inspecting, cleaning, extract, and modeling data with the intention of extracting useful information in order to support users in their decisions. With the advent of Big Data, data analysis was becoming more complicated due to the volume and variety of data. This process begins with the acquisition of the data and the selection of the data that is useful for the desiderata analysis. With such amount of data, also expert users are not able to inspect the data and understand if a dataset is suitable or not for their purposes. In this dissertation, we focus on five
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Arpteg, Anders. "Adaptive Semi-structured Information Extraction." Licentiate thesis, Linköping University, Linköping University, KPLAB - Knowledge Processing Lab, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5688.

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<p>The number of domains and tasks where information extraction tools can be used needs to be increased. One way to reach this goal is to construct user-driven information extraction systems where novice users are able to adapt them to new domains and tasks. To accomplish this goal, the systems need to become more intelligent and able to learn to extract information without need of expert skills or time-consuming work from the user.</p><p>The type of information extraction system that is in focus for this thesis is semistructural information extraction. The term semi-structural refers to docum
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Schierle, Martin. "Language Engineering for Information Extraction." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-81757.

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Accompanied by the cultural development to an information society and knowledge economy and driven by the rapid growth of the World Wide Web and decreasing prices for technology and disk space, the world\'s knowledge is evolving fast, and humans are challenged with keeping up. Despite all efforts on data structuring, a large part of this human knowledge is still hidden behind the ambiguities and fuzziness of natural language. Especially domain language poses new challenges by having specific syntax, terminology and morphology. Companies willing to exploit the information contained in such corp
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Lam, Man I. "Business information extraction from web." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1937939.

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Książki na temat "Information Extraction"

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Pazienza, Maria Teresa, ed. Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7.

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Maybury, Mark T., ed. Multimedia Information Extraction. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118219546.

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Paolo, Coletti, ed. Information extraction in finance. WIT Press, 2008.

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Turenne, Nicolas, and Jean-Charles Pomerol, eds. Knowledge Needs and Information Extraction. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574560.

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Gonzalez, Pablo Javier Barrio. Ranking for Scalable Information Extraction. [publisher not identified], 2015.

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Pazienza, Maria Teresa, ed. Information Extraction in the Web Era. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11781.

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Cortez, Eli, and Altigran S. da Silva. Unsupervised Information Extraction by Text Segmentation. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02597-1.

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Teresa, Pazienza Maria, ed. Information extraction: Towards scalable, adaptable systems. Springer-Verlag, 1999.

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Verfasser, Piryani Rajesh, and Singh Vivek Kumar Verfasser, eds. Applied Information Extraction and Sentiment Analysis. LAP LAMBERT Academic Publishing, 2015.

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Dahlke, Stephan, Wolfgang Dahmen, Michael Griebel, et al., eds. Extraction of Quantifiable Information from Complex Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08159-5.

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Części książek na temat "Information Extraction"

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Vilain, Marc. "Inferential Information Extraction." In Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_6.

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Wilks, Yorick, and Roberta Catizone. "Can We Make Information Extraction More Adaptive?" In Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_1.

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Chanod, Jean-Pierre. "Natural Language Processing and Digital Libraries." In Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_2.

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Voorhees, Ellen M. "Natural Language Processing and Information Retrieval." In Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_3.

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Dahl, Verónica. "From Speech to Knowledge." In Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_4.

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Sowa, John F. "Relating Templates to Language and Logic." In Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_5.

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Somers, Harold. "Knowledge Extraction from Bilingual Corpora." In Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_7.

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Basili, Roberto, Massimo Di Nanni, and Maria Teresa Pazienza. "Engineering of IE Systems: An Object-Oriented Approach." In Information Extraction. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48089-7_8.

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Nédellec, Claire, Adeline Nazarenko, and Robert Bossy. "Information Extraction." In Handbook on Ontologies. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92673-3_30.

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Ji, Heng. "Information Extraction." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_204-2.

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Streszczenia konferencji na temat "Information Extraction"

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Chiticariu, Laura, Yunyao Li, and Frederick R. Reiss. "Rule-Based Information Extraction is Dead! Long Live Rule-Based Information Extraction Systems!" In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/d13-1079.

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Intrator, Yotam, Regev Cohen, Ori Kelner, Roman Goldenberg, Ehud Rivlin, and Daniel Freedman. "Streamlining Conformal Information Retrieval via Score Refinement." In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.fever-1.22.

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Momii, Yuki, Tetsuya Takiguchi, and Yasuo Ariki. "RAG-Fusion Based Information Retrieval for Fact-Checking." In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.fever-1.4.

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Badieh Habib Morgan, Mena, and Maurice van Keulen. "Information Extraction for Social Media." In Proceedings of the Third Workshop on Semantic Web and Information Extraction. Association for Computational Linguistics and Dublin City University, 2014. http://dx.doi.org/10.3115/v1/w14-6202.

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Okurowski, Mary Ellen. "Information extraction overview." In a workshop. Association for Computational Linguistics, 1993. http://dx.doi.org/10.3115/1119149.1119164.

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Doan, AnHai, Raghu Ramakrishnan, and Shivakumar Vaithyanathan. "Managing information extraction." In the 2006 ACM SIGMOD international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1142473.1142595.

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Chiticariu, Laura, Yunyao Li, Sriram Raghavan, and Frederick R. Reiss. "Enterprise information extraction." In the 2010 international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1807167.1807339.

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Fan, Siqi, Yequan Wang, Jing Li, Zheng Zhang, Shuo Shang, and Peng Han. "Interactive Information Extraction by Semantic Information Graph." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/569.

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Information extraction (IE) mainly focuses on three highly correlated subtasks, i.e., entity extraction, relation extraction and event extraction. Recently, there are studies using Abstract Meaning Representation (AMR) to utilize the intrinsic correlations among these three subtasks. AMR based models are capable of building the relationship of arguments. However, they are hard to deal with relations. In addition, the noises of AMR (i.e., tags unrelated to IE tasks, nodes with unconcerned conception, and edge types with complicated hierarchical structures) disturb the decoding processing of IE.
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Milward, David, and James Thomas. "From information retrieval to information extraction." In the ACL-2000 workshop. Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/1117755.1117767.

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Wu, Fei, Raphael Hoffmann, and Daniel S. Weld. "Information extraction from Wikipedia." In the 14th ACM SIGKDD international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401978.

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Raporty organizacyjne na temat "Information Extraction"

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Etzioni, Oren. Open Information Extraction. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada538482.

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Cohen, Eric, and Evelyne Tzoukermann. Phrase-based Multimedia Information Extraction. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada456800.

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White, Michael, Tanya Korelsky, Claire Cardie, Vincent Ng, David Pierce, and Kiri Wagstaff. Multidocument Summarization via Information Extraction. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada457772.

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Onyshkevych, Boyan. Template Design for Information Extraction. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada635849.

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Srihari, Rohini, and Wei Li. Information Extraction Supported Question Answering. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada460042.

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Shinyama, Yusuke, and Satoshi Sekine. Paraphrase Acquisition for Information Extraction. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada460236.

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Irwin, N. H., S. M. DeLand, and S. V. Crowder. Extraction of information from unstructured text. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/148697.

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Nurre, Joseph H. Automate Information Extraction from Scan Data. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada362095.

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Principe, Jose C. Feature Extraction Using an Information Theoretic Framework. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada397483.

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Irwin, N. H. Domain-independent information extraction in unstructured text. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/378821.

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