--- title: "Named entity recognition (NER) — papers and Mova" description: "Named entity recognition (NER) in plain words: what it finds and why it is hard, how Mova knows people and places, and our cards of the papers on NER." canonical: "https://research.elamur.ai/free/en/topics/named-entity-recognition/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/topics/named-entity-recognition/) [home](https://md.research.elamur.ai/free/en/) / [topics](https://md.research.elamur.ai/free/en/topics/) / Named entity recognition # Named entity recognition 10 paper cards from the Research library ## What it is Named entity recognition, NER for short, finds the names in a text and says what kind of thing each one names: a person, a place, an organization, a date, an amount. In «Abraham went down to Egypt», a NER system marks «Abraham» as a person and «Egypt» as a place. It is harder than it looks. A name can be several words long, the same name can be a person or a company or a town, capital letters help in English but not in Hebrew, and new names appear every day. Systems learn from texts where people have marked the names, and they are measured by how many names they find and type exactly right. ## How Mova does it Mova has no separate NER tagger. Its tagger marks proper nouns in the dependency tree, the coreference sieve gives every mention a semantic class (person, place, organization, time and others) and uses it to decide what «it» or «she» can refer to, and the frames reader records who acts in every clause. The classes come with the reason they were chosen. - [coref — the mentions and their classes](https://mova.elamur.ai/free/en/docs/coref/) - [en — the tagger and parser](https://mova.elamur.ai/free/en/docs/en/) - [Frames: who did what to whom](https://mova.elamur.ai/free/en/docs/docs/pragmatics/) ## The papers (10) Cards with a measured or kept idea first, then by citations. 1. [AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With](https://md.research.elamur.ai/free/en/papers/langstream-2021-seker-alephbert-hebrew-large-pre-trained/) *— [Amit Seker](https://md.research.elamur.ai/free/en/authors/amit-seker/), [Elron Bandel](https://md.research.elamur.ai/free/en/authors/elron-bandel/), [Dan Bareket](https://md.research.elamur.ai/free/en/authors/dan-bareket/), [Idan Brusilovsky](https://md.research.elamur.ai/free/en/authors/idan-brusilovsky/) et al., 2021 · 22 citations* 2. [Neural Modeling for Named Entities and Morphology (NEMO2)](https://md.research.elamur.ai/free/en/papers/tacl-2021-bareket-neural-modeling-named-entities-morphology/) *— [Dan Bareket](https://md.research.elamur.ai/free/en/authors/dan-bareket/), [Reut Tsarfaty](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/), 2021 · 18 citations* 3. [Unsupervised AMR-Dependency Parse Alignment](https://md.research.elamur.ai/free/en/papers/chen-2017-unsupervised-amr-dependency-parse-alignment/) *— Wei-Te Chen, [Martha Palmer](https://md.research.elamur.ai/free/en/authors/martha-palmer/), 2017 · 13 citations* 4. [English Multiword Expression-aware Dependency Parsing Including Named Entities](https://md.research.elamur.ai/free/en/papers/aclbulk-2017-kato-english-multiword-expression-aware-dependency/) *— [Akihiko Kato](https://md.research.elamur.ai/free/en/authors/akihiko-kato/), [Hiroyuki Shindo](https://md.research.elamur.ai/free/en/authors/hiroyuki-shindo/), [Yuji Matsumoto](https://md.research.elamur.ai/free/en/authors/yuji-matsumoto/), 2017 · 4 citations* 5. [BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic](https://md.research.elamur.ai/free/en/papers/langstream-2022-chen-btpk-based-interpretable-method-ner/) *— Yulin Chen; Beishui Liao; Bruno Bentzen; Bo Yuan; Zelai Yao; Haixiao Chi;, Dov Gabbay, 2022 · 1 citations* 6. [Domain-Aware Error Correction for Citation NER in Medieval Hebrew Responsa](https://md.research.elamur.ai/free/en/papers/aclbulk-2026-liebeskind-domain-aware-error-correction-citation/) *— [Shmuel LIebeskind](https://md.research.elamur.ai/free/en/authors/shmuel-liebeskind/), [Maayan Zhitomirsky-Geffet](https://md.research.elamur.ai/free/en/authors/maayan-zhitomirsky-geffet/), Binyamin Katzoff, Nati Ben-Gigi et al., 2026* 7. [Expanding the Lexicon of Ge'ez Based African Languages: A Comparative Study of Amharic and Tigrinya](https://md.research.elamur.ai/free/en/papers/langstream-2026-teklehaymanot-expanding-lexicon-ge-ez-based/) *— [Hailay Kidu Teklehaymanot](https://md.research.elamur.ai/free/en/authors/hailay-kidu-teklehaymanot/), Debela Desalegn Yadeta, [Wolfgang Nejdl](https://md.research.elamur.ai/free/en/authors/wolfgang-nejdl/), 2026* 8. [Towards Named-Entity and Coreference Annotation of the Hebrew Bible](https://md.research.elamur.ai/free/en/papers/coref-2024-swanson-named-entity-coreference-annotation-hebrew/) *— [Daniel G. Swanson](https://md.research.elamur.ai/free/en/authors/daniel-g-swanson/), [Bryce D. Bussert](https://md.research.elamur.ai/free/en/authors/bryce-d-bussert/), [Francis Tyers](https://md.research.elamur.ai/free/en/authors/francis-tyers/), 2024* 9. [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://md.research.elamur.ai/free/en/papers/langstream-2023-gueta-explicit-morphological-knowledge-improves-pre/) *— [Eylon Gueta](https://md.research.elamur.ai/free/en/authors/eylon-gueta/), [Omer Goldman](https://md.research.elamur.ai/free/en/authors/omer-goldman/), [Reut Tsarfaty](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/), 2023* 10. [A Survey of Qualitative Error Analysis for Neural Machine Translation Systems](https://md.research.elamur.ai/free/en/papers/diaz-2020-survey-qualitative-error-analysis-neural/) *— Denise Díaz, James Cross, Vishrav Chaudhary, Ahmed Kishky et al., 2020* ## More papers in the library (65) The most cited first. [All 65 →](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/library/) - [The Stanford CoreNLP Natural Language Processing Toolkit](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/) — Christopher Manning, Mihai Surdeanu, John Bauer, et al. · Proceedings of 52nd Annual Meeting of the… - [Natural Language Processing (almost) from Scratch](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/) — Ronan Collobert, Jason Weston, Leon Bottou, et al. · arXiv:1103.0398 - [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/) — Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, et al. · arXiv:1901.08746 - [Bidirectional LSTM-CRF Models for Sequence Tagging](https://md.research.elamur.ai/free/en/papers/huang-2015-bidirectional-lstm-crf-models-sequence/) — Zhiheng Huang, Wei Xu, Kai Yu · arXiv:1508.01991 - [Word Representations: A Simple and General Method for Semi-Supervised Learning](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/) — Joseph Turian, Lev-Arie Ratinov, Yoshua Bengio · Proceedings of the 48th Annual Meeting of the… - [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://md.research.elamur.ai/free/en/papers/zhao-2018-gender-bias-coreference-resolution-evaluation/) — Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al. · Proceedings of the 2018 Conference of the North… - [CamemBERT: a Tasty French Language Model](https://md.research.elamur.ai/free/en/papers/martin-2019-camembert-tasty-french-language-model/) — Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, et al. · arXiv:1911.03894 · Proceedings of the 58th… - [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://md.research.elamur.ai/free/en/papers/ma-2016-end-end-sequence-labeling-bi/) — Xuezhe Ma, Eduard Hovy · arXiv:1603.01354 - [Contextual String Embeddings for Sequence Labeling](https://md.research.elamur.ai/free/en/papers/akbik-2018-contextual-string-embeddings-sequence-labeling/) — Alan Akbik, Duncan Blythe, Roland Vollgraf · Proceedings of the 27th International… - [Design Challenges and Misconceptions in Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/ratinov-2009-design-challenges-misconceptions-named-entity/) — Lev Ratinov, Dan Roth · Proceedings of the Thirteenth Conference on… - [← previous topic Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) - [next topic → Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) - [↑ back to topics](https://md.research.elamur.ai/free/en/topics/)