Datasets:

Karavet
/

pioNER-Armenian-Named-Entity

Sub-tasks: named-entity-recognition

Languages: Armenian

Multilinguality: monolingual

License:

Dataset card Files Files and versions Community

Dataset Viewer

View in Dataset Viewer

Viewer

The dataset viewer is not available for this dataset.

Cannot get the config names for the dataset.

Error code:   ConfigNamesError
Exception:    FileNotFoundError
Message:      Couldn't find a dataset script at /src/services/worker/Karavet/pioNER-Armenian-Named-Entity/pioNER-Armenian-Named-Entity.py or any data file in the same directory. Couldn't find 'Karavet/pioNER-Armenian-Named-Entity' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in Karavet/pioNER-Armenian-Named-Entity. 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response
                  for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory
                  raise FileNotFoundError(
              FileNotFoundError: Couldn't find a dataset script at /src/services/worker/Karavet/pioNER-Armenian-Named-Entity/pioNER-Armenian-Named-Entity.py or any data file in the same directory. Couldn't find 'Karavet/pioNER-Armenian-Named-Entity' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in Karavet/pioNER-Armenian-Named-Entity.

Need help to make the dataset viewer work? Open a discussion for direct support.

YAML Metadata Warning: The task_categories "named-entity-recognition" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

pioNER - named entity annotated datasets

pioNER corpus provides gold-standard and automatically generated named-entity datasets for the Armenian language.

Alongside the datasets, we release 50-, 100-, 200-, and 300-dimensional GloVe word embeddings trained on a collection of Armenian texts from Wikipedia, news, blogs, and encyclopedia.

Silver-standard dataset

The generated corpus is automatically extracted and annotated using Armenian Wikipedia. We used a modification of Nothman et al and Sysoev and Andrianov approaches to create this corpus. This approach uses links between Wikipedia articles to extract fragments of named-entity annotated texts.

The corpus is split into train and development sets.

Table 1. Statistics for pioNER train, development and test sets

dataset	#tokens	#sents	annotation	texts' source
train	130719	5964	automatic	Wikipedia
dev	32528	1491	automatic	Wikipedia
test	53606	2529	manual	iLur.am

Gold-standard dataset

This dataset is a collection of over 250 news articles from iLur.am with manual named-entity annotation. It includes sentences from political, sports, local and world news, and is comparable in size with the test sets of other languages (Table 2). We aim it to serve as a benchmark for future named entity recognition systems designed for the Armenian language.

The dataset contains annotations for 3 popular named entity classes: people (PER), organizations (ORG), and locations (LOC), and is released in CoNLL03 format with IOB tagging scheme. During annotation, we generally relied on categories and guidelines assembled by BBN Technologies for TREC 2002 question answering track

Tokens and sentences were segmented according to the UD standards for the Armenian language from ArmTreebank project.

Table 2. Comparison of pioNER gold-standard test set with test sets for English, Russian, Spanish and German

test dataset	#tokens	#LOC	#ORG	#PER
Armenian pioNER	53606	1312	1338	1274
Russian factRuEval-2016	59382	1239	1595	1353
German CoNLL03	51943	1035	773	1195
Spanish CoNLL02	51533	1084	1400	735
English CoNLL03	46453	1668	1661	1671

Downloads last month: 69

Edit dataset card