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Journal articles on the topic 'Information Extraction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Aumann, Yonatan, Ronen Feldman, Yair Liberzon, Benjamin Rosenfeld, and Jonathan Schler. "Visual information extraction." Knowledge and Information Systems 10, no. 1 (2006): 1–15. http://dx.doi.org/10.1007/s10115-006-0014-x.

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Naoe, Kensuke, Hideyasu Sasaki, and Yoshiyasu Takefuji. "Information Hiding by Machine Learning." International Journal of Organizational and Collective Intelligence 2, no. 1 (2011): 21–48. http://dx.doi.org/10.4018/ijoci.2011010102.

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In this paper, the authors propose information hiding by machine learning: a method of key generation for information extracting using neural network. The method consists of three layers for information hiding. First, the proposed method prepares feature extraction keys, which are saved by feature extraction attributes like feature coordinates and the region of frequency coefficients. Second, the proposed method prepares hidden patterns in advance to the embedding procedure as a watermark signal of the target contents. Finally, the proposed method generates information extraction keys by using

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Zhu, Wenhao, Chaoyou Ju, Wei Xu, Jiaoxiong Xia, and Li Fu. "Extracting Medical Records with Hierarchical Information Extraction Method." Information Technology Journal 12, no. 18 (2013): 4441–46. http://dx.doi.org/10.3923/itj.2013.4441.4446.

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14

Pang, Yueyong, Chi Zhang, Liangchen Zhou, Bingxian Lin, and Guonian Lv. "Extracting Indoor Space Information in Complex Building Environments." ISPRS International Journal of Geo-Information 7, no. 8 (2018): 321. http://dx.doi.org/10.3390/ijgi7080321.

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Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor

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15

Gutierrez, Fernando, Dejing Dou, Stephen Fickas, Daya Wimalasuriya, and Hui Zong. "A hybrid ontology-based information extraction system." Journal of Information Science 42, no. 6 (2016): 798–820. http://dx.doi.org/10.1177/0165551515610989.

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Information Extraction is the process of automatically obtaining knowledge from plain text. Because of the ambiguity of written natural language, Information Extraction is a difficult task. Ontology-based Information Extraction (OBIE) reduces this complexity by including contextual information in the form of a domain ontology. The ontology provides guidance to the extraction process by providing concepts and relationships about the domain. However, OBIE systems have not been widely adopted because of the difficulties in deployment and maintenance. The Ontology-based Components for Information

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Zhang, Zhi Ming, Shuai Shuai Huang, and Ping Li. "A Web Information Extraction Method Based on HTML Parser." Advanced Materials Research 774-776 (September 2013): 1802–6. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1802.

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With the rapid development of Internet, and surge in the amount of information on the Internet, how to accurately and quickly get the information of the users really need, such as the title, links, and pictures, is the hotspot. This paper proposed a fast web information extraction method based on html parser, this paper validated the effect of the proposed method by extracting commodities information of e-commerce website, the results show that the accuracy of the information extraction by our method is higher than the extraction method based on regular expressions, and the extraction time is

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Zhu, Dengyun, Qi Guo, Dongjiao Zhang, and Fucheng Wan. "Information Extraction Research Review." Journal of Physics: Conference Series 1769, no. 1 (2021): 012004. http://dx.doi.org/10.1088/1742-6596/1769/1/012004.

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18

Thompson, Paul. "Concept-Based Information Extraction." Integrated Computer-Aided Engineering 1, no. 6 (1994): 537–46. http://dx.doi.org/10.3233/ica-1994-1606.

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Murphy, A. J., N. W. Ratcliffe, D. A. H. Johnson, and D. J. Dewhurst. "Information Extraction from Speech." Annals of Otology, Rhinology & Laryngology 96, no. 1_suppl (1987): 69–71. http://dx.doi.org/10.1177/00034894870960s135.

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As an approach to the separation of the information in speech from speaker-specific attributes, we have used a modified cepstral analysis followed by a peak-tracking process in the cepstral domain. The resulting tracks show a high degree of similarity between different male speakers saying the same words and is a basis for speaker-independent word recognition. The method does not involve pitch frequency determination.

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20

Wang, Daisy Zhe, Michael J. Franklin, Minos Garofalakis, and Joseph M. Hellerstein. "Querying probabilistic information extraction." Proceedings of the VLDB Endowment 3, no. 1-2 (2010): 1057–67. http://dx.doi.org/10.14778/1920841.1920974.

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21

Gregg, Dawn G., and Steven Walczak. "Adaptive web information extraction." Communications of the ACM 49, no. 5 (2006): 78–84. http://dx.doi.org/10.1145/1125944.1125945.

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22

Craven, Mark. "Information extraction from Medline." ACM SIGBIO Newsletter 18, no. 3 (1998): 10. http://dx.doi.org/10.1145/956034.956049.

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23

Nakatani, Tomofumi, and Shogo Nakamura. "Extraction of phonemic information." Journal of the Acoustical Society of America 95, no. 1 (1994): 590. http://dx.doi.org/10.1121/1.408271.

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24

BOULAKNADEL, Siham, and Fadoua ATAA ALLAH. "Amazigh Biographical Information Extraction." International Journal of Knowledge Engineering and Data Mining 8, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijkedm.2023.10053903.

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Allah, Fadoua Ataa, and Siham Boulaknadel. "Amazigh biographical information extraction." International Journal of Knowledge Engineering and Data Mining 8, no. 1 (2023): 27–42. http://dx.doi.org/10.1504/ijkedm.2023.135715.

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26

Gregg, Dawn. "Exploring Information Extraction Resilience." JUCS - Journal of Universal Computer Science 14, no. (11) (2008): 1911–20. https://doi.org/10.3217/jucs-014-11-1911.

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There are many challenges developers face when attempting to reliably extract data from the Web. One of these challenges is the resilience of the extraction system to changes in the web pages information is being extracted from. This article compares the resilience of information extraction systems that use position based extraction with an ontology based extraction system and a system that combines position based extraction with ontology based extraction. The findings demonstrate the advantages of using a system that combines multiple extraction techniques, especially in environments where we

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27

Zhang, Junyan, Junchen Zhou, Liqin Zhou, and Zhichao Ba. "Extracting Multifaceted Characteristics of Patients With Chronic Disease Comorbidity: Framework Development Using Large Language Models." JMIR Medical Informatics 13 (May 15, 2025): e70096. https://doi.org/10.2196/70096.

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Background Research on chronic multimorbidity has increasingly become a focal point with the aging of the population. Many studies in this area require detailed patient characteristic information. However, the current methods for extracting such information are complex, time-consuming, and prone to errors. The challenge of quickly and accurately extracting patient characteristics has become a common issue in the study of chronic disease comorbidities. Objective Our objective was to establish a comprehensive framework for extracting demographic and disease characteristics of patients with multi

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28

Aguilar, Carlos, Lydia Sánchez, and Manuel Campos. "Extraction of Information of Audio-Visual Contents." tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 9, no. 2 (2011): 543–50. http://dx.doi.org/10.31269/triplec.v9i2.311.

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In this article we show how it is possible to use Channel Theory (Barwise and Seligman, 1997) for modeling the process of information extraction realized by audiences of audio-visual contents. To do this, we rely on the concepts pro- posed by Channel Theory and, especially, its treatment of representational systems. We then show how the information that an agent is capable of extracting from the content depends on the number of channels he is able to establish between the content and the set of classifications he is able to discriminate. The agent can endeavor the extraction of information thr

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Aguilar, Carlos, Lydia Sánchez, and Manuel Campos. "Extraction of Information of Audio-Visual Contents." tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 9, no. 2 (2011): 543–50. http://dx.doi.org/10.31269/vol9iss2pp543-550.

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In this article we show how it is possible to use Channel Theory (Barwise and Seligman, 1997) for modeling the process of information extraction realized by audiences of audio-visual contents. To do this, we rely on the concepts pro- posed by Channel Theory and, especially, its treatment of representational systems. We then show how the information that an agent is capable of extracting from the content depends on the number of channels he is able to establish between the content and the set of classifications he is able to discriminate. The agent can endeavor the extraction of information thr

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30

Mohamed Ali El-Morsy, Sally, Mahmoud Hussein, and Hamdy M. Mousa. "Arabic open information extraction system using dependency parsing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 541. http://dx.doi.org/10.11591/ijece.v12i1.pp541-551.

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<p>Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsi

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Sally, Mohamed Ali El-Morsy, Hussein Mahmoud, and M. Mousa Hamdy. "Arabic open information extraction system using dependency parsing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 541–51. https://doi.org/10.11591/ijece.v12i1.pp541-551.

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Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsing and th

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Wang, Yuyu, Gang Li, Wei Tang, Yussif M. Awelisah, and Ling Lin. "A Dynamic Spectrum extraction method for extracting blood scattering information — Dual-position extraction method." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 221 (October 2019): 116965. http://dx.doi.org/10.1016/j.saa.2019.03.075.

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Akimov, Dmitry Aleksandrovich. "Automatic Extraction of Scientific Information from Open Access Publications." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (2020): 383–91. http://dx.doi.org/10.5373/jardcs/v12sp3/20201273.

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P. Vishwakarma, Virendra, Sujata Pandey, and M. N. Gupta. "Fuzzy based Pixel wise Information Extraction for Face Recognition." International Journal of Engineering and Technology 2, no. 1 (2010): 117–23. http://dx.doi.org/10.7763/ijet.2010.v2.109.

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Scholtz, Brenda, Thashen Padayachy, and Oluwande Adewoyin. "Pilot Testing of an Information Extraction (IE) Prototype for Legal Research." African Journal of Information and Communication, no. 25 (June 30, 2020): 1–20. http://dx.doi.org/10.23962/10539/29192.

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This article presents findings from pilot testing of elements of an information extraction (IE) prototype designed to assist legal researchers in engaging with case law databases. The prototype that was piloted seeks to extract, from legal case documents, relevant and accurate information on cases referred to (CRTs) in the source cases. Testing of CRT extraction from 50 source cases resulted in only 38% (n = 19) of the extractions providing an accurate number of CRTs. In respect of the prototype’s extraction of CRT attributes (case title, date, journal, and action), none of the 50 extractions

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Zhang, Zhiwei, Wenbo Qin, and Haifeng Xu. "A Visual Webpage Information Extraction Framework For Competitive Intelligence System." Scalable Computing: Practice and Experience 25, no. 5 (2024): 4138–52. http://dx.doi.org/10.12694/scpe.v25i5.3078.

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The extraction of webpage information is of paramount importance in the realm of competitive intelligence. This research is dedicated to the design and implementation of a visual webpage information extraction module within a competitive intelligence system, approached through the lens of research and development (R&D) technology and its practical applications. Initially, the study delineates the objectives and requirements for webpage information extraction, emphasizing the practical needs of competitive intelligence systems. By critically assessing the strengths and weaknesses of current

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Gu, Jun Hua, Jie Song, Na Zhang, and Yan Liu Liu. "A Method of Web Information Automatic Extraction Based on XML." Applied Mechanics and Materials 20-23 (January 2010): 178–83. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.178.

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With the increasingly high-speed of the internet as well as the increase in the amount of data it contains, users are finding it more and more difficult to gain useful information from the web. How to extract accurate information from the Web efficiently has become an urgent problem. Web information extraction technology has emerged to solve this kind of problem. The method of Web information auto-extraction based on XML is designed through standardizing the HTML document using data translation algorism, forming an extracting rule base by learning the XPath expression of samples, and using ext

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Jain, Devika, Jeff Blossom, Jack Hayes, Heike Gibson, Sheryl Rifas-Shimann, and Diane R. Gold. "RINX 2.0: A Containerized Climate Raster Information Extraction System on OpenShift Cloud Environment." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-G-2025 (July 10, 2025): 391–95. https://doi.org/10.5194/isprs-annals-x-g-2025-391-2025.

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Abstract. RINX (Raster INformation eXtraction) 2.0 is an advanced solution for efficiently extracting climate data from large raster datasets in a cloud computing environment. Building upon the original RINX 1.0, which utilized high-performance computing clusters, RINX 2.0 leverages cloud technologies such as OpenShift and PostGIS to handle massive datasets and automate the extraction process. The system supports large-scale spatiotemporal raster extractions, processing over 158 million data points from the 15TB PRISM climate dataset. Here, we describe the architecture, methods, and tools used

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Wang, Haitao, Tong Zhu, Mingtao Wang, Guoliang Zhang, and Wenliang Chen. "A Prior Information Enhanced Extraction Framework for Document-level Financial Event Extraction." Data Intelligence 3, no. 3 (2021): 460–76. http://dx.doi.org/10.1162/dint_a_00103.

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Document-level financial event extraction (DFEE) is the task of detecting events and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the financial domain. This task is challenging as the financial documents are generally long text and event arguments of one event may be scattered in different sentences. To address this issue, we proposed a novel Prior Information Enhanced Extraction framework (PIEE) for DFEE, leveraging prior information from both event types and pre-trained language models. Specifically, PIEE cons

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Wang, Zixiang, Tongliang Li, and Zhoujun Li. "Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures." Electronics 12, no. 9 (2023): 1977. http://dx.doi.org/10.3390/electronics12091977.

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Numerical information plays an important role in various fields such as scientific, financial, social, statistics, and news. Most prior studies adopt unsupervised methods by designing complex handcrafted pattern-matching rules to extract numerical information, which can be difficult to scale to the open domain. Other supervised methods require extra time, cost, and knowledge to design, understand, and annotate the training data. To address these limitations, we propose QuantityIE, a novel approach to extracting numerical information as structured representations by exploiting syntactic feature

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Seffi, Gebeyehu, Wolde Worke, and S. Shibesh Zelalem. "Information extraction model from Ge'ez texts." Information extraction model from Ge'ez texts 30, no. 2 (2023): 787–95. https://doi.org/10.11591/ijeecs.v30.i2.pp787-795.

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Nowadays, voluminous and unstructured textual data is found on the Internet that could provide varied valuable information for different institutions such as health care, business-related, training, religion, culture, and history, among others. A such alarming growth of unstructured data fosters the need for various methods and techniques to extract valuable information from unstructured data. However, exploring helpful information to satisfy the needs of the stakeholders becomes a problem due to information overload via the internet. This paper, therefore, presents an effective model for extr

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Sethi, Ganesh K., and Rajesh K. Bawa. "Text Information Extraction: in Context of Indian Languages." Advanced Materials Research 433-440 (January 2012): 5012–19. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5012.

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Text data present in images contains useful information and its extraction involves detection, localization, extraction, enhancement and recognition. However, the problem is challenging due to fact that text can have various styles, size, orientations, alignments, effect of lighting conditions. While a large number of techniques have been proposed in the past for extracting text from images and video frames for foreign languages, not much research has been carried out for Indian languages. The purpose of this paper is to review various algorithms for the problem for foreign as well as for the

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Kuttaiyapillai, Dhanasekaran, Anand Madasamy, Shobanadevi Ayyavu, and Md Shohel Sayeed. "Clinical named entity extraction for extracting information from medical data." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1722. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1722-1731.

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Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine le

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Dhanasekaran, Kuttaiyapillai Anand Madasamy Shobanadevi Ayyavu Md Shohel Sayeed. "Clinical named entity extraction for extracting information from medical data." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1722–31. https://doi.org/10.11591/ijeecs.v35.i3.pp1722-1731.

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Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the trad

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Jusoh, Shaidah, and Hejab M. Alfawareh. "Opinion analysis from reviews using information extraction and fuzzy sets." International Journal of Academic Research 6, no. 2 (2014): 253–58. http://dx.doi.org/10.7813/2075-4124.2014/6-2/a.36.

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Lehnert, W., C. Cardie, D. Fisher, J. McCarthy, E. Riloff, and S. Soderland. "Evaluating an Information Extraction System." Integrated Computer-Aided Engineering 1, no. 6 (1994): 453–72. http://dx.doi.org/10.3233/ica-1994-1601.

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Fiori, Alessandro, Alberto Grand, Giulia Bruno, Francesco Gavino Brundu, Domenico Schioppa, and Andrea Bertotti. "Information Extraction from Microarray Data." Journal of Database Management 25, no. 1 (2014): 29–58. http://dx.doi.org/10.4018/jdm.2014010102.

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Nowadays, a huge amount of high throughput molecular data are available for analysis and provide novel and useful insights into complex biological systems, through the acquisition of a high-resolution picture of their molecular status in defined experimental conditions. In this context, microarrays are a powerful tool to analyze thousands of gene expression values with a single experiment. A number of approaches have been developed to detecting genes highly correlated to diseases, selecting genes that exhibit a similar behavior under specific conditions, building models to predict disease outc

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48

Kim, Jaeyoon. "Legislative Research Using Information Extraction." Korean Journal of Law and Economics 19, no. 1 (2022): 1–18. http://dx.doi.org/10.46758/kjle.2022.04.19.1.1.

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Chong, William, and Angela Goh. "FIES: Financial Information Extraction System." Information Services & Use 17, no. 4 (1997): 215–23. http://dx.doi.org/10.3233/isu-1997-17401.

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Hope, Carol J., Jennifer Hornung Garvin, and Brian C. Sauer. "Information extraction from narrative data." American Journal of Health-System Pharmacy 69, no. 6 (2012): 455–61. http://dx.doi.org/10.2146/ajhp110135.

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