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24 results for “named entity recognition”
Enhancing Name Entity Recognition Through Hybrid Deep Learning in Natural Language Processing
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Named Entity Recognition in the Regesta of Emperor Frederick III. of the Holy Roman Empire
<p>This dataset contains Named Entity Recognition and Entity Linking across 8883 Regesta of Emperor Frederick III. of the Holy Roman Empire. The Regesta are from the Regesta Imperii Edition project: http://www.regesta-imperii.de/en/the-project.html.</p> <p>There are 4554 different names normalized to 2849 distinct entities, identified by the index entry identifier of the yet to be fully published index of the Regesta of Frederick III. All in all, 17258 instances of names are identified.</p> <p><strong>Not all named entities are identified!</strong></p> <p>This dataset is intended as a step stone for recording already identified entities and training NER classifiers for the Regesta Imperii. Some named entities were deliberately omitted.</p> <p>Only those that could be identified with reasonable certainty via the index were included, for example: "Mgf. Albrecht von Brandenburg" is included, while "Albrecht", even when refering to the same entity as the former name, is not. Even of this subset, some entities may have been missed. A newer version may rectify that in the future.</p> <p>The JSONL format is modeled after the <a href="https://github.com/doccano/doccano">Doccano</a> format for entity linking.</p>
Temporally-Informed Analysis of Named Entity Recognition
<p>This repository contains the data set developed for the paper:</p> <p>“Shruti Rijhwani and Daniel Preoțiuc-Pietro. <em>Temporally-Informed Analysis of Named Entity Recognition.</em> In Proceedings of the Association for Computational Linguistics (ACL). 2020.”</p> <p>It includes 12,000 tweets annotated for the named entity recognition task. The tweets are uniformly distributed over the years 2014-2019, with 2,000 tweets from each year. The goal is to have a temporally diverse corpus to account for data drift over time when building NER models.</p> <p>The entity types annotated are locations (LOC), persons (PER) and organizations (ORG). The tweets are preprocessed to replace usernames and URLs with a unique token. Hashtags are left intact and can be annotated as named entities.</p> <p><strong>Format</strong></p> <p>The repository contains the annotations in JSON format.</p> <p>Each year-wise file has the tweet IDs along with token-level annotations. The Public Twitter Search API (<a href="https://developer.twitter.com/en/docs/tweets/search">https://developer.twitter.com/en/docs/tweets/search</a>) can be used extract the text for the tweet corresponding to the tweet IDs.</p> <p><strong>Data Splits</strong></p> <p>Typically, NER models are trained and evaluated on annotations available at the model building time, but are used to make predictions on data from a future time period. This setup makes the model susceptible to temporal data drift, leading to lower performance on future data as compared to the test set.</p> <p>To examine this effect, we use tweets from the years 2014-2018 as the training set and random splits of the 2019 tweets as the development and test sets. These splits simulate the scenario of making predictions on data from a future time period.</p> <p>The development and test splits are provided in the JSON format.</p> <p><strong>Use</strong></p> <p>Please cite the data set and the accompanying paper if you found the resources in this repository useful.</p>
Named Entity Recognition Datasets
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.