Browse State-of-the-Art › Named Entity Recognition (NER)
Named Entity Recognition (NER)
955 papers with code · 76 benchmarks · 130 datasets archive 2025-07-28
Named Entity Recognition (NER) is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
Example:
| Mark | Watney | visited | Mars |
|---|---|---|---|
| B-PER | I-PER | O | B-LOC |
( Image credit: Zalando )
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
76 leaderboard tables shown for this task, 76 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 76 until expanded.
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
130 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 130 until expanded.
Subtasks archive 2025-07-28
13 subtasks in the archive's task tree.
Most implemented papers archive 2025-07-28
30 shown of 955 papers with code (2,874 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
-
11 Oct 2018 534 repositories listed Syntology ran 204 of 659 samples · 455 unverified · 149 pointer-only (licence)We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
-
15 Feb 2018 46 repositories listed Syntology ran 23 of 58 samples · 35 unverified · 25 pointer-only (licence)We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.
-
4 Mar 2016 43 repositories listed Syntology ran 10 of 36 samples · 26 unverified · 6 pointer-only (licence)State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.
-
4 Mar 2016 25 repositories listed Syntology ran 4 of 24 samples · 20 unverified · 3 pointer-only (licence)State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.
-
9 Aug 2015 25 repositories listed Syntology ran 0 of 8 samples · 8 unverifiedIt can also use sentence level tag information thanks to a CRF layer.
-
19 Apr 2019 19 repositories listed Syntology ran 0 of 7 samples · 7 unverifiedWe present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).
-
25 Jan 2019 19 repositories listed Syntology ran 4 of 25 samples · 21 unverified · 1 pointer-only (licence)Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
-
5 Jun 2020 14 repositories listed Syntology ran 4 of 13 samples · 9 unverified · 3 pointer-only (licence)Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
-
26 Nov 2015 14 repositories listed Syntology ran 4 of 9 samples · 5 unverified · 4 pointer-only (licence)Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
-
31 Aug 2019 10 repositories listedThe pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on…
-
2 Oct 2020 9 repositories listed Syntology ran 3 of 10 samples · 7 unverifiedIn this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
-
10 Nov 2019 8 repositories listedWe show that the use of web crawled data is preferable to the use of Wikipedia data.
-
25 Oct 2019 8 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 2 pointer-only (licence)Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
-
16 May 2021 7 repositories listed Syntology ran 1 of 7 samples · 6 unverifiedIn this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.
-
10 Nov 2019 6 repositories listed Syntology ran 0 of 1 samples · 1 unverifiedThe Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.
-
26 Mar 2019 6 repositories listedObtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
-
20 Jun 2018 6 repositories listed Syntology ran 1 of 13 samples · 12 unverified · 4 pointer-only (licence)Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
-
21 Jul 2017 6 repositories listedSelecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
-
8 Dec 2020 5 repositories listedCross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.
-
29 Jul 2020 5 repositories listedWe introduce biomedical and clinical English model packages for the Stanza Python NLP library.
-
16 Mar 2020 5 repositories listed Syntology ran 16 of 30 samples · 14 unverified · 28 pointer-only (licence)We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
-
29 Aug 2018 5 repositories listedWe introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
-
31 Jul 2017 5 repositories listedIn this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.
-
18 Apr 2022 4 repositories listedIn this paper, we propose \textbf{LayoutLMv3} to pre-train multimodal Transformers for Document AI with unified text and image masking.
-
20 May 2021 4 repositories listedWe introduce Korean Language Understanding Evaluation (KLUE) benchmark.
-
24 Oct 2020 4 repositories listedWe re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models.
-
30 Jul 2020 4 repositories listedNamed Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens.
-
28 Feb 2020 4 repositories listedRecently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus.
-
7 Nov 2019 4 repositories listed Syntology ran 0 of 3 samples · 3 unverifiedMany NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples…
-
8 Sep 2019 4 repositories listed Syntology ran 0 of 7 samples · 7 unverifiedWe examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.
Syntology lines on 17 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections