Message Understanding Conference-6: a brief history

Towards Bangla Named Entity Recognition

Chowdhury, S. A., Alam, F., & Khan, N. (2018) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccitechn.2018.8631931 · Cited by 16

Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models

Nguyen, T. B., Nguyen, Q. M., Nguyen, T. T. H., Truong, Q., & Luong, C. M. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1896 · Cited by 16

HisVA: A Visual Analytics System for Studying History

Han, D., Parsad, G., Kim, H., Shim, J., Kwon, O., Son, K. A., Lee, J., Cho, I., & Ko, S. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3086414 · Cited by 18

Event detection based on open information extraction and ontology

Sahnoun, S., Elloumi, S., & Yahia, S. B. (2020) · Journal of Information and Telecommunication · doi:10.1080/24751839.2020.1763007 · Cited by 16

Entity Ranking on Graphs: Studies on Expert Finding

Rode, H., Serdyukov, P., Hiemstra, D., & Zaragoza, H. (2007) · Cited by 16

A hybrid method for Persian Named Entity Recognition

Ahmadi, F., & Moradi, H. (2015) · International Conference on Information and Knowledge Technology (IKT) · doi:10.1109/ikt.2015.7288806 · Cited by 16

Named entity recognition for Sinhala language

Dahanayaka, J. K., & Weerasinghe, A. R. (2014) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter.2014.7083904 · Cited by 16

An Environment for the Joint Management of Written Policies and Business Rules

Lévy, F., Guisse, A., Nazarenko, A., Omrane, N., & Szulman, S. (2010) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2010.95 · Cited by 16

The pattern of name tokens in narrative clinical text and a comparison of five systems for redacting them

Kayaalp, M., Browne, A. C., Callaghan, F. M., Dodd, Z. A., Divita, G., Ozturk, S., & McDonald, C. J. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001689 · Cited by 16

Information extraction of public complaints on Twitter text for bandung government

Anggareska, D., & Purwarianti, A. (2014) · International Conference on Data and Software Engineering · doi:10.1109/icodse.2014.7062658 · Cited by 16

Person Entity Recognition for the Indonesian Qur’an Translation with the Approach Hidden Markov Model-Viterbi

M.A.K., R. M. S., Bijaksana, M. A., & Huda, A. F. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.08.160 · Cited by 16

General learning approach for event extraction: Case of management change event

Elloumi, S., Jaoua, A., Ferjani, F., Semmar, N., Besançon, R., Alja’am, J. M., & Hammami, H. (2012) · Journal of Information Science · doi:10.1177/0165551512464140 · Cited by 16

Anticipatory Event Detection via Sentence Classification

He, Q., Chang, K., & Lim, E. (2006) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2006.384554 · Cited by 16

Enhancing Semantic Search using N-Levels Document Representation

Basile, P., Caputo, A., de Gemmis, M., Gentile, A. L., Lops, P., & Semeraro, G. M. (2008) · MADOC (University of Mannheim) · Cited by 16

Designing Annotation Schemes: From Model to Representation

Ide, N., Chiarcos, C., Stede, M., & Cassidy, S. (2017) · Springer eBooks · doi:10.1007/978-94-024-0881-2_3 · Cited by 15

Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-trained Language Models

Yang, G., Dineen, S., Lin, Z., & Liu, X. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-87839-9_3 · Cited by 17

Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and Nahuatl

Hernández, A., Ortega-Mendoza, R. M., Villatoro-Tello, E., Camacho-Bello, C., & Pérez-Cortes, O. (2024) · Mathematics · doi:10.3390/math12081136 · Cited by 16

Specializing for predicting obesity and its co-morbidities

Goldstein, I., & Uzuner, Ö. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.11.001 · Cited by 15

Nerit: Named Entity Recognition for Informal Text

Etter, D., Ferraro, F., Cotterell, R., Buzek, O., Durme, B. V., Ferraro, F., & Van, B. (2012) · Cited by 15

A Resource-Based Method for Named Entity Extraction and Classification

Gamallo, P., & García, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24769-9_44 · Cited by 15

Information extraction from microblogs: a survey

Hua, W., Huynh, D., Hosseini, S., Lu, J., & Zhou, X. (2012) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 15

Named entity recognition on Indonesian microblog messages

Taufik, N., Wicaksono, A. F., & Adriani, M. (2016) · International Conference on Asian Language Processing · doi:10.1109/ialp.2016.7876005 · Cited by 15

What should entity linking link

Rosales-Méndez, H., Poblete, B., & Hogan, A. (2018) · Universidad de Chile · Cited by 15

Identification of Rules for Recognition of Named Entity Classes in Mizo Language

Bentham, J., Pakray, P., Majumder, G., Lalbiaknia, S., & Gelbukh, A. F. (2016) · Mexican International Conference on Artificial Intelligence · doi:10.1109/micai-2016.2016.00010 · Cited by 15

Does semantics aid syntax? An empirical study on named entity recognition and classification

Zhong, X., Cambria, E., & Hussain, A. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05949-0 · Cited by 15

Graph-based Semantic Relatedness for Named Entity Disambiguation

Gentile, A. L., Zhang, Z., Xia, L., & Iria, J. (2009) · Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) · Cited by 15

Multilingual resources for entity extraction

Strassel, S. M., Mitchell, A., & Huang, S. (2003) · doi:10.3115/1119384.1119391 · Cited by 15

Entity and Aspect Extraction for Organizing News Comments

Prasojo, R. E., Kacimi, M., & Nutt, W. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806576 · Cited by 15

Evaluation of Named Entity Features for Punjabi Language

Kaur, A., & Josan, G. S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.02.007 · Cited by 15

Second HAREM: New Challenges and Old Wisdom

Santos, D., Freitas, C., Oliveira, H. G., & Carvalho, P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85980-2_22 · Cited by 15

Tree-Structured Named Entity Recognition on OCR Data: Analysis, Processing and Results

Dinarelli, M., & Rosset, S. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2ohz7zaf3475 · Cited by 15

Named Entity Linking in a Complex Domain: Case Second World War History

Heino, E., Tamper, M., Eetu Mäkelä, A., Leskinen, P., Ikkala, E., Tuominen, J., Koho, M., & Eero Hyvönen, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59888-8_10 · Cited by 15

The search problem posed by large heterogeneous data sets in litigation

Baron, J. R., & Thompson, P. (2007) · International Conference on Artificial Intelligence and Law · doi:10.1145/1276318.1276344 · Cited by 15

Extraction and Representation of Financial Entities from Text

Repke, T., & Krestel, R. (2021) · Springer eBooks · doi:10.1007/978-3-030-66891-4_11 · Cited by 16

skweak: Weak Supervision Made Easy for NLP

Lison, P., Barnes, J., & Hubin, A. (2021) · NORA - Norwegian Open Research Archives · Cited by 14

ADMit: Improving NER in automotive domain with domain adversarial training and multi-task learning

Park, C., Jeong, S., & Kim, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120007 · Cited by 14

Extracting and structuring information from the electronic medical text: state of the art and trendy directions

Landolsi, M., Hlaoua, L., & Romdhane, L. B. (2023) · Multimedia tools and applications · doi:10.1007/s11042-023-15080-y · Cited by 12

Deep learning-based automatic analysis of legal contracts: a named entity recognition benchmark

Aejas, B., Belhi, A., Zhang, H., & Bouras, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09869-7 · Cited by 14

Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

Guo, Y., Huang, C., Sheng, Y., Zhang, W., Ye, X., Lian, H., Xu, J., & Chen, Y. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02587-z · Cited by 15

A Semi-supervised Learning Approach to Arabic Named Entity Recognition

Althobaiti, M. J., Kruschwitz, U., & Poesio, M. (2013) · Open Access at Essex (University of Essex) · Cited by 14

Text Segmentation and Graph-based Method for Template Filling in Information Extraction

Jean‐Louis, L., Besançon, R., & Ferret, O. (2011) · Cited by 14

Semi-Supervised Bootstrapping Approach for Named Entity Recognition

Thenmalar, S., Balaji, J. M., & Geetha, T. V. (2015) · International Journal on Natural Language Computing · doi:10.5121/ijnlc.2015.4501 · Cited by 19

Named and Specific Entity Detection in Varied Data: The Quaero Named Entity Baseline Evaluation

Galibert, O., Quintard, L., Rosset, S., Zweigenbaum, P., Nédellec, C., Aubin, S., Gillard, L., Raysz, J.-P., Pois, D., Tannier, X., et al. (2010) · HAL · Cited by 14

Portuguese Named Entity Recognition using Conditional Random Fields and Local Grammars

Pirovani, J. C. M., & de Oliveira, E. S. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/45ggu5xzexsm · Cited by 14

A Methodology towards Effective and Efficient Manual Document Annotation: Addressing Annotator Discrepancy and Annotation Quality

Zhang, Z., Chapman, S., & Ciravegna, F. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16438-5_21 · Cited by 14

Automatic system for identifying and categorizing temporal relations in natural language

Llorens, H., Saquete, E., & Navarro-Colorado, B. (2012) · International Journal of Intelligent Systems · doi:10.1002/int.21542 · Cited by 14

Improvement of n-ary Relation Extraction by Adding Lexical Semantics to Distant-Supervision Rule Learning

Hong, L., Krause, S., Xu, F., Moro, A., Uszkoreit, H., & Roberto Navigli, A. (2015) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0005187303170324 · Cited by 14

Uncertainty Reduction for Knowledge Discovery and Information Extraction on the World Wide Web

Ji, H., Deng, H., & Han, J. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2190489 · Cited by 15

A Graph-based Cross-lingual Projection Approach for Weakly Supervised Relation Extraction

Kim, S., & Lee, G. G. (2012) · Cited by 14

Diamonds in the Rough: Event Extraction from Imperfect Microblog Data

Intxaurrondo, A., Agirre, E., de Lacalle, O. L., & Surdeanu, M. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1066 · Cited by 14

A Relational Framework for Information Extraction

Fagin, R., Kimelfeld, B., Reiss, F. R., & Vansummeren, S. (2016) · ACM SIGMOD Record · doi:10.1145/2935694.2935696 · Cited by 14

Ananya - a Named-Entity-Recognition (NER) system for Sinhala language

Manamini, S. A. P. M., Ahamed, A., Rajapakshe, R. A. E. C., Reemal, G. H. A., Jayasena, S., Dias, G., & Ranathunga, S. (2016) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon.2016.7480111 · Cited by 14

A Broad-coverage Corpus for Finnish Named Entity Recognition

Luoma, J., Oinonen, M., Pyykönen, M., Laippala, V., & Pyysalo, S. (2020) · Language Resources and Evaluation · Cited by 14

An approach to protein name extraction using heuristics and a dictionary

Seki, K., & Mostafa, J. (2003) · Proceedings of the American Society for Information Science and Technology · doi:10.1002/meet.1450400109 · Cited by 14

Experience with a Language Engineering Architecture: Three Years of GATE

Cunningham, H., Gaizauskas, R., Humphreys, K., & Wilks, Y. A. (1999) · Cited by 14

Generative AI-Based Text Generation Methods Using Pre-Trained GPT 2 ModelPreprint

Pandey, R., Waghela, H., Rakshit, S., Rangari, A., Singh, A., Kumar, R., Ghoshal, R., & Sen, J. (2024) · doi:10.36227/techrxiv.171216659.95569463/v1 · Cited by 15

A pilot investigation of information extraction in the semantic annotation of archaeological reports

Vlachidis, A., & Tudhope, D. S. (2012) · International Journal of Metadata Semantics and Ontologies · doi:10.1504/ijmso.2012.050183 · Cited by 13

Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

Watson, E. ‘., Viana, T., & Zhang, S. (2023) · AI · doi:10.3390/ai4010007 · Cited by 18

BioBBC: a multi-feature model that enhances the detection of biomedical entities

Alamro, H., Gojobori, T., Essack, M., & Gao, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-58334-x · Cited by 15

A Hybrid Approach for Persian Named Entity Recognition

Moradi, H., Ahmadi, F., & Feizi‐Derakhshi, M. (2017) · Iranian Journal of Science and Technology Transactions A Science · doi:10.1007/s40995-017-0209-x · Cited by 13

Automatic Event Reference Identification

March, O., & Baldwin, T. (2008) · Cited by 13

First Order Hidden Markov Model for Automatic Arabic Name Entity Recognition

Dahan, F., Touir, A. A., & Mathkour, H. I. (2015) · International Journal of Computer Applications · doi:10.5120/ijca2015905397 · Cited by 13

Improving NER for Clinical Texts by Ensemble Approach using Segment Representations.

Nayel, H., & Shashirekha, H. L. (2017) · International conference natural language processing · Cited by 13

Mention detection in coreference resolution: survey

Lata, K., Singh, P., & Dutta, K. (2022) · Applied Intelligence · doi:10.1007/s10489-021-02878-2 · Cited by 14

A multi-level methodology for the automated translation of a coreference resolution dataset: an application to the Italian language

Minutolo, A., Guarasci, R., Damiano, E., Pietro, G. D., Fujita, H., & Esposito, M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07641-3 · Cited by 13

ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer

Jibril, E. C., & Tantug, A. C. (2023) · IEEE Access · doi:10.1109/access.2023.3243468 · Cited by 14

Generalizability and comparison of automatic clinical text de-identification methods and resources.

Ferrández, Ó., South, B. R., Shen, S., Friedlin, J., Samore, M. H., & Meystre, S. M. (2012) · PubMed Central · Cited by 13

Creating A Corpus of Plagiarised Academic Texts

Stevenson, M., & Clough, P. D. (2009) · Cited by 13

Automatically generated NE tagged corpora for English and Hungarian

Simon, E., & Nemeskey, D. M. (2012) · SZTAKI Publication Repository (Hungarian Academy of Sciences) · Cited by 13

Cross-Lingual Annotation Projection for Weakly-Supervised Relation Extraction

Kim, S., Jeong, M., Lee, J., & Lee, G. G. (2014) · ACM Transactions on Asian Language Information Processing · doi:10.1145/2529994 · Cited by 13

CAPER: Collaborative Information, Acquisition, Processing, Exploitation and Reporting for the Prevention of Organised Crime

Aliprandi, C., Irujo, J. A., Cuadros, M., Maier, S., Melero, F., & Raffaelli, M. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-07857-1_26 · Cited by 13

Introduction to Information Extraction: Basic Notions and Current Trends

Balke, W. (2012) · Datenbank-Spektrum · doi:10.1007/s13222-012-0090-x · Cited by 13

Semi-automatic construction of a named entity dictionary for entity-based sentiment analysis in social media

Song, Y., Jeong, S., & Kim, H. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3445-8 · Cited by 13

Cross-document event ordering through temporal, lexical and distributional knowledge

Navarro-Colorado, B., & Saquete, E. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.07.032 · Cited by 13

Improved deep belief network model and its application in named entity recognition of Chinese electronic medical records

Li, W., Shi, S., Gao, Z., Wei, W., Zhu, Q., Lin, X., Jiang, D., & Gao, S. B. (2018) · IEEE International Conference on Big Data Analysis (ICBDA) · doi:10.1109/icbda.2018.8367707 · Cited by 13

Fictive motion extraction and classification

Egorova, E., Moncla, L., Gaio, M., Claramunt, C., & Purves, R. S. (2018) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2018.1498503 · Cited by 13

Extraktion relationaler Daten aus Texten

Diesner, J., & Carley, K. M. (2010) · VS Verlag für Sozialwissenschaften eBooks · doi:10.1007/978-3-531-92575-2_44 · Cited by 13

Boosting a Semantic Search Engine by Named Entities

Caputo, A., Basile, P., & Semeraro, G. M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04125-9_27 · Cited by 13

Information Extraction

Kejriwal, M. (2019) · SpringerBriefs in computer science · doi:10.1007/978-3-030-12375-8_2 · Cited by 13

Filter Keywords and Majority Class Strategies for Company Name Disambiguation in Twitter

Spina, D., Amigó, E., & Gonzalo, J. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23708-9_7 · Cited by 12

Widening the field of view of information extraction through sentential event recognition

Patwardhan, S. (2010) · Cited by 12

Characterizing Interactions and Relationships between People

Rashid, F., & Blanco, E. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1470 · Cited by 12

Neural Relation Extraction within and across Sentence Boundaries

Gupta, P., Rajaram, S., Schütze, H., & Runkler, T. A. (2019) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v33i01.33016513 · Cited by 107

Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military

Liao, F., Ma, L., Pei, J., & Tan, L. (2019) · Future Internet · doi:10.3390/fi11080180 · Cited by 12

Semantic Enrichment for Recommendation of Primary Studies in a Systematic Literature Review

Rizzo, G. F., Tomassetti, F., Vetrò, A., Ardito, L., Torchiano, M., Morisio, M., & Raphaël Troncy, A. (2015) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqv031 · Cited by 12

Event representation across genre

Pivovarova, L., Huttunen, S., & Yangarber, R. (2013) · Cited by 12

Improving Toponym Disambiguation by Iteratively Enhancing Certainty of Extraction

Habib, M. B., & van Keulen, M. (2012) · International Conference on Knowledge Discovery and Information Retrieval · doi:10.5220/0004174903990410 · Cited by 12

The Second Open Knowledge Extraction Challenge

Nuzzolese, A. G., Gentile, A. L., Valentina Presutti, A., Aldo Gangemi, A., Meusel, R., & Paulheim, H. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-46565-4_1 · Cited by 12

A Dataset for Open Event Extraction in English

Nguyen, K.-H., Tannier, X., Ferret, O., & Besançon, R. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/459q7qvijdoi · Cited by 12

Development of semi-supervised named entity recognition to discover new tourism places

Saputro, K. E., Kusumawardani, S. S., & Fauziati, S. (2016) · International Conference on Science and Technology (ICST) · doi:10.1109/icstc.2016.7877360 · Cited by 12

A New Classification Framework to Evaluate the Entity Profiling on the Web

Barforoush, A. A., Shirazi, H. M., & Emami, H. (2017) · ACM Computing Surveys · doi:10.1145/3066904 · Cited by 12

A Survey on Named Entity Recognition Solutions Applied for Cybersecurity-Related Text Processing

Georgescu, T.-M., Iancu, B., Zamfiroiu, A., Doinea, M., Boja, C., & Cartas, C. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5859-7_31 · Cited by 12

A Hybrid Approach for Robust Multilingual Toponym Extraction and Disambiguation

Habib, M. B., & van Keulen, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38634-3_1 · Cited by 12

Learning thematic role relations for lexical semantic nets

Wagner, A. (2005) · Cited by 12

Unsupervised learning of mDTD extraction patterns for Web text mining

Kim, D., Jung, H., & Lee, G. G. (2003) · Information Processing & Management · doi:10.1016/s0306-4573(03)00004-9 · Cited by 12

Methods for Collection and Evaluation of Comparable Documents

Paramita, M. L., Guthrie, D., Kanoulas, E., Gaizauskas, R., Clough, P. D., & Sanderson, M. (2013) · Springer eBooks · doi:10.1007/978-3-642-20128-8_5 · Cited by 11

TaughtNet: Learning Multi-Task Biomedical Named Entity Recognition From Single-Task Teachers

Moscato, V., Postiglione, M., Sansone, C., & Sperlí, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3244044 · Cited by 12

Preserving Privacy in Arabic Judgments: AI-Powered Anonymization for Enhanced Legal Data Privacy

Moussaoui, T. E., Loqman, C., & Boumhidi, J. (2023) · IEEE Access · doi:10.1109/access.2023.3324288 · Cited by 13

Database principles in information extraction

Kimelfeld, B. (2014) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/2594538.2594563 · Cited by 11

E-Dictionaries and Finite-State Automata for the Recognition of Named Entities

Krstev, C., Vitas, D., Obradović, I., & Utvić, M. (2011) · Cited by 11

Design of the MUC-6 evaluation

Grishman, R., & Sundheim, B. M. (1995) · Message Understanding Conference (MUC) · doi:10.3115/1072399.1072401 · Cited by 107

THE MUC EVALUATION S

Ralph Grishma, N., & Beth Sundhei, M. (2014)

grishman©cs, nyu. edu

Grishman, R., & Sundheim, B. (2013)

Evaluating message understanding systems

ChinchorNancy, LewisDavid, D., & HirschmanLynette (1993) · Computational Linguistics · doi:10.5555/972487.972488 · Cited by 13

Message Understanding Conference (MUC) 6 Additional News Text

Chinchor, N. A., & Sundheim, B. (2020) · Americanae (AECID Library) · doi:10.35111/ajtx-gn82