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Multilingual NER Dataset

Multilingual NER Dataset for Named Entity Recognition

By Babelscape (From Huggingface) [source]

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About this dataset

> The Babelscape/wikineural NER Dataset is a comprehensive and diverse collection of multilingual text data specifically designed for the task of Named Entity Recognition (NER). It offers an extensive range of labeled sentences in nine different languages: French, German, Portuguese, Spanish, Polish, Dutch, Russian, English, and Italian. > > Each sentence in the dataset contains tokens (words or characters) that have been labeled with named entity recognition tags. These tags provide valuable information about the type of named entity each token represents. The dataset also includes a language column to indicate the language in which each sentence is written. > > This dataset serves as an invaluable resource for developing and evaluating NER models across multiple languages. It encompasses various domains and contexts to ensure diversity and representativeness. Researchers and practitioners can utilize this dataset to train and test their NER models in real-world scenarios. > > By using this dataset for NER tasks, users can enhance their understanding of how named entities are recognized across different languages. Furthermore, it enables benchmarking performance comparisons between various NER models developed for specific languages or trained on multiple languages simultaneously. > > Whether you are an experienced researcher or a beginner exploring multilingual NER tasks, the Babelscape/wikineural NER Dataset provides a highly informative and versatile resource that can contribute to advancements in natural language processing and information extraction applications on a global scale

How to use the dataset

> > > - Understand the Data Structure: > - The dataset consists of labeled sentences in nine different languages: French (fr), German (de), Portuguese (pt), Spanish (es), Polish (pl), Dutch (nl), Russian (ru), English (en), and Italian (it). > - Each sentence is represented by three columns: tokens, ner_tags, and lang. > - The tokens column contains the individual words or characters in each labeled sentence. > - The ner_tags column provides named entity recognition tags for each token, indicating their entity types. > - The lang column specifies the language of each sentence. > > - Explore Different Languages: > - Since this dataset covers multiple languages, you can choose to focus on a specific language or perform cross-lingual analysis. > - Analyzing multiple languages can help uncover patterns and differences in named entities across various linguistic contexts. > > - Preprocessing and Cleaning: > - Before training your NER models or applying any NLP techniques to this dataset, it's essential to preprocess and clean the data. > - Consider removing any unnecessary punctuation marks or special characters unless they carry significant meaning in certain languages. > > - Training Named Entity Recognition Models: > 4a. Data Splitting: Divide the dataset into training, validation, and testing sets based on your requirements using appropriate ratios. > 4b. Feature Extraction: Prepare input features from tokenized text data such as word embeddings or character-level representations depending on your model choice. > 4c. Model Training: Utilize state-of-the-art NER models (e.g., LSTM-CRF, Transformer-based models) to train on the labeled sentences and ner_tags columns. > 4d. Evaluation: Evaluate your trained model's performance using the provided validation dataset or test datasets specific to each language. > > - Applying Pretrained Models: > - Instead of training a model from scratch, you can leverage existing pretrained NER models like BERT, GPT-2, or SpaCy's named entity recognition capabilities. > - Fine-tune these pre-trained models on your specific NER task using the labeled

Research Ideas

> - Training NER models: This dataset can be used to train NER models in multiple languages. By providing labeled sentences and their corresponding named entity recognition tags, the dataset can help train models to accurately identify and classify named entities in different languages. > - Evaluating NER performance: The dataset can be used as a benchmark to evaluate the performance of pre-trained or custom-built NER models. By using the labeled sentences as test data, developers and researchers can measure the accuracy, precision, recall, and F1-score of their models across multiple languages. > - Cross-lingual analysis: With labeled sentences available in nine different languages, researchers can perform cross-lingual analysis on named entities across different language datasets. This can provide insights into how certain types of entities are referred to or categorized across various cultures and linguistic contexts

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication > No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: test_fr.csv

Column name	Description
tokens	This column contains individual words or characters present in each sentence. (Text)
ner_tags	This column specifies the named entity recognition tags associated with each token. These tags indicate whether a token represents a person's name, organization, location, date, or other entities. (Text)
lang	This column indicates the language of the sentences. (Text)

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File: val_de.csv

Column name	Description
tokens	This column contains individual words or characters present in each sentence. (Text)
ner_tags	This column specifies the named entity recognition tags associated with each token. These tags indicate whether a token represents a person's name, organization, location, date, or other entities. (Text)
lang	This column indicates the language of the sentences. (Text)

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File: test_de.csv

Column name	Description
tokens	This column contains individual words or characters present in each sentence. (Text)
ner_tags	This column specifies the named entity recognition tags associated with each token. These tags indicate whether a token represents a person's name, organization, location, date, or other entities. (Text)
lang	This column indicates the language of the sentences. (Text)

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Babelscape (From Huggingface).