Message Understanding Conference-6: a brief history

The CONCISUS Corpus of Event Summaries

Saggion, H., & Szasz, S. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2pqhkav6byou · Cited by 11

Fine-Grained Evaluation for Entity Linking

Rosales-Méndez, H., Hogan, A., & Poblete, B. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1066 · Cited by 11

NERSE: Named Entity Recognition in Software Engineering as a Service

Reddy, M. V. P., Prasad, P. V. R. D., Chikkamath, M., & Mandadi, S. (2019) · Lecture notes in business information processing · doi:10.1007/978-3-030-32242-7_6 · Cited by 11

Named Entity Recognition of Ancient Poems Based on Albert‐BiLSTM‐MHA‐CRF Model

Zhou, F., Wang, C., & Wang, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/6507719 · Cited by 11

Automatic compilation of language resources for named entity recognition in Turkish by utilizing Wikipedia article titles

Küçük, D. (2015) · Computer Standards & Interfaces · doi:10.1016/j.csi.2015.02.003 · Cited by 11

Digital Library Technologies: Complex Objects, Annotation, Ontologies, Classification, Extraction, and Security

Fox, E. A., & Torres, R. D. S. (2014) · Synthesis lectures on information concepts, retrieval, and services · doi:10.2200/s00566ed1v01y201401icr033 · Cited by 11

REX-J: Japanese referring expression corpus of situated dialogs

Spanger, P., Yasuhara, M., Iida, R., Tokunaga, T., Terai, A., & Kuriyama, N. (2010) · Language Resources and Evaluation · doi:10.1007/s10579-010-9134-8 · Cited by 11

Automated data extraction from the web with conditional models

Phan, X.-H., Horiguchi, S., & Ho, T. B. (2005) · International Journal of Business Intelligence and Data Mining · doi:10.1504/ijbidm.2005.008362 · Cited by 11

CRF+LG: A Hybrid Approach for the Portuguese Named Entity Recognition

Pirovani, J. C. M., & de Oliveira, E. S. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-76348-4_11 · Cited by 11

A Method of Chinese Named Entity Recognition Based on CNN-BILSTM-CRF Model

Sun, L., Yuan, R., Yi, L., & Li, X. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-2206-8_15 · Cited by 11

The Role of Trust and Automation in an Intelligence Analyst Decisional Guidance Paradigm

Neigel, A. R., Caylor, J. P., Kase, S. E., Vanni, M., & Hoye, J. (2018) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343418799601 · Cited by 11

What was the Question? A Systematization of Information Retrieval and NLP Problems

Dörpinghaus, J., Darms, J., & Jacobs, M. D. (2018) · Annals of Computer Science and Information Systems · doi:10.15439/2018f168 · Cited by 11

Implicit entity linking in tweets: An ad-hoc retrieval approach

Hosseini, H., Nguyen, T. T., Wu, J. M., & Ebrahim Bagheri, A. (2019) · Applied Ontology · doi:10.3233/ao-190215 · Cited by 11

An Environment for Named Entity Recognition and Translation

Graliński, F., Jassem, K., & Marcińczuk, M. (2009) · Cited by 11

A Pattern and POS Auto-Learning Method for Terminology Extraction from Scientific Text

Shao, W., Bolin, H., & Song, L. (2021) · Data and Information Management · doi:10.2478/dim-2021-0005 · Cited by 11

Telugu named entity recognition using bert

Gorla, S., Tangeda, S. S., Neti, L. B. M., & Malapati, A. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00305-w · Cited by 13

Search Result Clustering Using Informatively Named Entities

Toda, H., Kataoka, R., & Oku, M. (2007) · International Journal of Human-Computer Interaction · doi:10.1080/10447310701360995 · Cited by 11

Towards a context model driven german geo-tagging system

Blessing, A., Kuntz, R., & Schütze, H. (2007) · Workshop on Geographic Information Retrieval (GIR) · doi:10.1145/1316948.1316956 · Cited by 11

A Refined TF-IDF Algorithm Based on Channel Distribution Information for Web News Feature Extraction

Xu, M., He, L., & Lin, X. A. (2010) · International Workshop on Education Technology and Computer Science · doi:10.1109/etcs.2010.130 · Cited by 11

Ontology-Driven Information Extraction

Miksch, S., & Yildiz, B. (2007) · Cited by 11

Named Entity Recognition in Turkish: Approaches and Issues

Küçük, D., Arıcı, N., & Küçük, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59569-6_20 · Cited by 11

Cross-Language Learning for Arabic Relation Extraction

Taghizadeh, N., Faili, H., & Maleki, J. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.475 · Cited by 11

Information Extraction Based on Deep Syntactic-Semantic Analysis

Андреевич, С. Д., Budnikov, E. A., Stepanova, M., Matavina, P. V., & Chelombeeva, A. N. (2016) · Cited by 10

Classification ensemble to improve medical Named Entity Recognition

Keretna, S., Lim, C. P., Creighton, D., & Shaban, K. B. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974324 · Cited by 10

MCORES: a system for noun phrase coreference resolution for clinical records

Bodnari, A., Szolovits, P., & Uzuner, Ö. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000591 · Cited by 10

Named entity recognition in Assamese: A hybrid approach

Sharma, P., Sharma, U., & Kalita, J. K. (2016) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2016.7732364 · Cited by 10

Point‐of‐interest detection from Weibo data for map updating

Yang, X., Gao, J., Zheng, X., Fang, M., Tang, L., & Zhang, X. (2022) · Transactions in GIS · doi:10.1111/tgis.12982 · Cited by 10

Qualitative analysis of workflow modifications used to generate the reference standard for the 2010 i2b2/VA challenge.

South, B. R., Shen, S., Barrus, R., DuVall, S. L., Uzuner, Ö., & Weir, C. R. (2011) · PubMed Central · Cited by 10

Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural Language

Dakle, P. P., Kadıoğlu, S., Uppuluri, K., Politi, R., Raghavan, P., Rallabandi, S. K., & Srinivasamurthy, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33271-5_20 · Cited by 15

Categorizing events using spatio-temporal and user features from Flickr

Canneyt, S. V., Schockaert, S., & Dhoedt, B. (2015) · Information Sciences · doi:10.1016/j.ins.2015.08.032 · Cited by 10

Employing Constituent Dependency Information for Tree Kernel-Based Semantic Relation Extraction between Named Entities

Qian, L., Zhou, G., & Zhu, Q. (2011) · ACM Transactions on Asian Language Information Processing · doi:10.1145/2002980.2002985 · Cited by 10

A’laam Corpus: A Standard Corpus of Named Entity for Persian Language

Hosseinnejad, S., Shekofteh, Y., & Azadi, T. E. (2017) · Signal and Data Processing · doi:10.29252/jsdp.14.3.127 · Cited by 10

Natural Language Data Management and Interfaces

Li, Y., & Rafiei, D. (2018) · Synthesis Lectures on Data Management · doi:10.1007/978-3-031-01862-6 · Cited by 14

A framework for crime data analysis using relationship among named entities

Das, P., Das, A. K., Nayak, J., & Pelusi, D. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04150-8 · Cited by 10

Advances in Computational Linguistics

Loizides, F., Gahl, S., Davies, E. C., Patrick, J. D., Miller, G., & Terzian, G. (2020) · doi:10.4236/9781649972710 · Cited by 10

SSAR-GNN: Self-Supervised Artist Recommendation from spatio-temporal perspectives in art history with Graph Neural Networks

Zhang, Q., Wang, M., Wang, H., Rao, X., & Chen, L. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.03.003 · Cited by 10

A Named Entity Recognition approach for Albanian

Skënduli, M. P., & Biba, M. (2013) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2013.6637407 · Cited by 10

A crime reports analysis system to identify related crimes

Ku, C., & Gondy Leroy, A. (2011) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21552 · Cited by 10

Leveraging word confusion networks for named entity modeling and detection from conversational telephone speech

Kurata, G., Itoh, N., Nishimura, M., Sethy, A., & Ramabhadran, B. (2011) · Speech Communication · doi:10.1016/j.specom.2011.11.002 · Cited by 10

Semantic analysis in the automation of ER modelling through natural language processing

Omar, N., Hanna, P., & Kevitt, P. M. (2006) · International Conference on Computing & Informatics · doi:10.1109/icoci.2006.5276559 · Cited by 10

BCCWJ-TimeBank: Temporal and Event Information Annotation on Japanese Text

Asahara, M., Yasuda, S., Konishi, H., Imada, M., & Maekawa, K. (2013) · Institutional Repositories DataBase (IRDB) · Cited by 10

A methodology for using professional knowledge in corpus annotation

Pustejovsky, J. D., & Stubbs, A. (2013) · Cited by 11

A Brief Sketch of the Historic Development of Pragmatics

Mey, J. L. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0027 · Cited by 12

Information extraction and speech recognition

Grishman, R. (1998) · Cited by 10

Named entity recognition: challenges in document annotation, gazetteer construction and disambiguation

Zhang, Z. (2013) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · Cited by 9

Developing a System for the Automated Coding of Protest Event DataPreprint

Hanna, A. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2425232 · Cited by 10

Improving Multi-Word Entity Recognition for Biomedical TextsPreprint

Nayel, H., Shashirekha, H. L., Shindo, H., & Matsumoto, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05691 · Cited by 16

Examining Knowledge Extraction Processes from Heterogeneous Data Sources

Sarıkoz, S. K. (2023) · Brilliant Engineering · doi:10.36937/ben.2023.4798 · Cited by 9

Enhancing Low Resource NER using Assisting Language and Transfer Learning

Sabane, M., Ranade, A., Litake, O., Patil, P., Joshi, R. B., & Kadam, D. (2023) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic56838.2023.10141204 · Cited by 12

Handwritten text recognition and information extraction from ancient manuscripts using deep convolutional and recurrent neural network

Bahi, H. E. (2024) · Soft Computing · doi:10.1007/s00500-024-09930-6 · Cited by 9

Uzbek news corpus for named entity recognition

Yusufu, A., Aziz, K., Yusufu, A., Ainiwaer, A., Li, F., & Ji, D. (2024) · Language Resources and Evaluation · doi:10.1007/s10579-024-09786-0 · Cited by 10

Adaptive graph clustering based on feature adversarial and graph transformer

Zhang, W., Zhu, X., Shang, R., Ren, J., & Jiao, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127541 · Cited by 9

Pedagogical Grammars Before the Eighteenth Century

Luhtala, A. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0015 · Cited by 9

Towards an Italian Healthcare Knowledge Graph

Postiglione, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89657-7_29 · Cited by 10

Cross-Language Latent Relational Search between Japanese and English Languages Using a Web Corpus

Duc, N. T., Bollegala, D., & Ishizuka, M. (2012) · ACM Transactions on Asian Language Information Processing · doi:10.1145/2334801.2334805 · Cited by 9

Merging Temporal Annotations

Llorens, H., UzZaman, N., & Allen, J. F. (2012) · International Symposium on Temporal Representation and Reasoning · doi:10.1109/time.2012.14 · Cited by 9

Domain-Specific Entity Linking via Fake Named Entity Detection

Zhang, J., Li, J., Li, X., Shi, Y., Li, J., & Wang, Z. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-32025-0_7 · Cited by 9

Incident-Driven Machine Translation and Name Tagging for Low-resource Languages

Hermjakob, U., Li, Q., Marcu, D., May, J., Mielke, S. J., Pourdamghani, N., Pust, M., Shi, X., Knight, K. K., Levinboim, T., et al. (2017) · Machine Translation · doi:10.1007/s10590-017-9207-1 · Cited by 9

Analysis of Different Supervised Techniques for Named Entity Recognition

Goyal, A., Gupta, V., & Kumar, M. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-0108-1_18 · Cited by 9

SiNER: A Large Dataset for Sindhi Named Entity Recognition

Ali, W., Lu, J., & Xu, Z. (2020) · Language Resources and Evaluation · Cited by 9

Knowledge Graphs

Kejriwal, M. (2022) · Tourism on the verge · doi:10.1007/978-3-030-88389-8_20 · Cited by 9

The Role of Query Sessions in Extracting Instance Attributes from Web Search Queries

PAŞCA, M. A., Alfonseca, E., Robledo-Arnuncio, E., Martin-Brualla, R., & Hall, K. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12275-0_9 · Cited by 9

Combining Data-Driven Systems for Improving Named Entity Recognition

Kozareva, Z., Ferrández, Ó., Montoyo, A., Muñoz, R., & Cueto, A. S. (2005) · Lecture notes in computer science · doi:10.1007/11428817_8 · Cited by 9

Timed annotations

Tomanek, K., & Hahn, U. (2009) · Linguistic Annotation Workshop · doi:10.3115/1698381.1698399 · Cited by 9

VnLoc: A Real -- Time News Event Extraction Framework for Vietnamese

Tran, M.-V., Nguyen, M., Nguyen, S.-Q., Nguyen, M.-T., & Phan, X.-H. (2012) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2012.34 · Cited by 9

Information Extraction

Nédellec, C., Nazarenko, A., & Bossy, R. (2009) · Springer eBooks · doi:10.1007/978-3-540-92673-3_30 · Cited by 9

Resources for Named Entity Recognition and Resolution in News Wires

Stern, R., & Sagot, B. (2010) · Cited by 9

Evaluating Automatically Generated Timelines from the Web

Catizone, R., Dalli, A., & Wilks, Y. A. (2006) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2ymnrahyry4e · Cited by 9

Knowledge mining from textual sources

Hahn, U., & Schnattinger, K. (1997) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/266714.266865 · Cited by 9

A Hybrid Approach to Semi-supervised Named Entity Recognition in Health, Safety and Environment Reports

Sari, Y. A. P., Hassan, M. F., & Zamin, N. B. (2009) · International Conference on Future Computer and Communication · doi:10.1109/icfcc.2009.52 · Cited by 9

The MITRE logical form generation system

Bayer, S., Burger, J. D., Greiff, J., & Wellner, B. (2004) · Cited by 9

Weakly supervised graph-based methods for classification

Radev, D. (2004) · Cited by 9

A Dependency Parsing Approach to Biomedical Text Mining

Pyysalo, S. (2008) · UTUPub (University of Turku) · Cited by 9

Semantic Indexing via Knowledge Organization Systems: Applying the CIDOC-CRM to Archaeological Grey Literature

Vlachidis, A. (2012) · UCL Discovery (University College London) · Cited by 9

Mind the GAP: A Balanced Corpus of Gendered Ambiguous PronounsPreprint

Webster, K., Recasens, M. V., Axelrod, V., & Baldridge, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.05201 · Cited by 9

Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in DanishPreprint

Plank, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.02931 · Cited by 9

Semantically-Informed Syntactic Machine Translation: A Tree-Grafting ApproachPreprint

Baker, K., Bloodgood, M., Callison-Burch, C., Dorr, B. J., Filardo, N. W., Levin, L. S., Miller, S., & Piatko, C. (2010) · arXiv (Cornell University) · doi:10.13016/m2h59f · Cited by 9

Query-Biased Text Summarization as a Question-Answering Technique

Chali, Y., Matwin, S., & Szpakowicz, S. (1999) · Cited by 9

STFL: Utilizing a Semi-Supervised, Transfer-Learning, Federated-Learning Approach to Detect Phishing URL Attacks

Sakazi, I., Grolman, E., Elovici, Y., & Shabtai, A. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650184 · Cited by 10

Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances

Park, Y., Son, G., & Rho, M. (2024) · Applied Sciences · doi:10.3390/app14209302 · Cited by 14

An Unsupervised Learning Approach for NER Based on Online Encyclopedia

Li, M., Yang, Q., He, F., Li, Z., Zhao, P., Zhao, L., & Chen, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26072-9_25 · Cited by 8

A Named Entity Recognition Method Based on Deep Learning For Chinese Legal Documents

Shi, J., Zheng, K., Zhang, Z., & Liu, Q. (2022) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc55077.2022.9887060 · Cited by 8

Full-span named entity recognition with boundary regression

Yu, J., Chen, Y., Zheng, Q., Wu, Y., & Chen, P. (2023) · Connection Science · doi:10.1080/09540091.2023.2181483 · Cited by 9

Using Temporal Cues for Segmenting Texts into Events

Jean‐Louis, L., Besançon, R., & Ferret, O. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14770-8_18 · Cited by 8

Mining the Web to Monitor the Political Consensus

Neri, F., Aliprandi, C., & Camillo, F. (2011) · Lecture notes in social networks · doi:10.1007/978-3-7091-0388-3_19 · Cited by 8

Named Entity Recognition in 140 Characters or Less.

Geyer, K., Greenfield, K., Mensch, A., & Simek, O. (2016) · Cited by 8

Linguistics in India

Scharf, P. M. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0012 · Cited by 8

A Named Entity Recognition Approach for Albanian Using Deep Learning

Trandafili, E., Meçe, E. K., & Duka, E. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-36617-9_6 · Cited by 8

Named Entity Recognition for Icelandic: Annotated Corpus and Models

Ingólfsdóttir, S. L., Guðjónsson, Á. A., & Loftsson, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59430-5_4 · Cited by 9

Research on Named Entity Recognition Methods in Chinese Forest Disease Texts

Wang, Q., & Xi-you, S. (2022) · Applied Sciences · doi:10.3390/app12083885 · Cited by 8

Named Entity Recognition for Addresses: An Empirical Study

Čeović, H., Kurdija, A. S., Delač, G., & Šilić, M. (2022) · IEEE Access · doi:10.1109/access.2022.3167418 · Cited by 10

Named Entity Recognition for Algerian Arabic Dialect in Social Media

Dahou, A. H., & Chéragui, M. A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-25344-7_13 · Cited by 8

Comparing Named Entity Recognition on Transcriptions and Written Texts

Alam, F., Magnini, B., & Zanoli, R. (2015) · Studies in computational intelligence · doi:10.1007/978-3-319-14206-7_4 · Cited by 7

Inflection of Polish Multi-Word Proper Names with Morfeusz and Multiflex

Savary, A., Rabiega-Wiśniewska, J., & Woliński, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04735-0_5 · Cited by 8

Linguistique et reconnaissance automatique des noms propres

Friburger, N. (2006) · Meta Journal des traducteurs · doi:10.7202/014331ar · Cited by 8

A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia

Woodward, D., Witmer, J., & Kalita, J. K. (2010) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2010.74 · Cited by 8

Handling Conjunctions in Named Entities

Dale, R., & Mazur, P. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-70939-8_12 · Cited by 8

Stalker, A Multilingual Text Mining Search Engine for Open Source Intelligence

Neri, F., & Pettoni, M. (2008) · International Conference on Information Visualisation (IV) · doi:10.1109/iv.2008.9 · Cited by 8

The History of Writing as a History of Linguistics

Daniels, P. T. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0003 · Cited by 9

A Partial String Matching Approach for Named Entity Recognition in Unstructured Bengali Data

Ibtehaz, N., & Satter, A. (2018) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2018.01.04 · Cited by 8

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