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Deep Learning model to extract entities from unstructured text. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: May 27, 2022 Item updated: Jan 2, 2025 Number of downloads: 2,532

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Description

This deep learning model is used to identify or categorize entities in unstructured text. An entity may refer to a word or a sequence of words such as the name of “Organizations,” “Persons,” “Country,” or “Date” and “Time” in the text. This model detects entities from the given text and classifies them into pre-determined categories.
Named entity recognition (NER) is useful when a high-level overview of a large quantity of text is required. NER can let you know crucial and important information in text by extracting the main entities from it. The extracted entities are categorized into pre-determined classes and can help in drawing meaningful decisions and conclusions.

Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check the Deep Learning Libraries Installer for ArcGIS.

Fine-tuning the model
This model cannot be fine-tuned using ArcGIS tools.

Input
Text files on which named entity extraction will be performed.

Output
Classified tokens into the following pre-defined entity classes:
  • PERSON – People, including fictional
  • NORP – Nationalities or religious or political groups
  • FACILITY – Buildings, airports, highways, bridges, etc.
  • ORGANIZATION – Companies, agencies, institutions, etc.
  • GPE – Countries, cities, states
  • LOCATION – Non-GPE locations, mountain ranges, bodies of water
  • PRODUCT – Vehicles, weapons, foods, etc. (Not services)
  • EVENT – Named hurricanes, battles, wars, sports events, etc.
  • WORK OF ART – Titles of books, songs, etc.
  • LAW – Named documents made into laws
  • LANGUAGE – Any named language
  • DATE – Absolute or relative dates or periods
  • TIME – Times smaller than a day
  • PERCENT – Percentage (including “%”)
  • MONEY – Monetary values, including unit
  • QUANTITY – Measurements, as of weight or distance
  • ORDINAL – “first,” “second”
  • CARDINAL – Numerals that do not fall under another type

Model architecture
This model uses the XLM-RoBERTa architecture implemented in Hugging Face transformers using the TNER library.

Accuracy metrics
This model has an accuracy of 91.6 percent.

Training data
The model has been trained on the OntoNotes Release 5.0 dataset.

Sample results
Here are a few results from the model.

Citations
Weischedel, Ralph, et al. OntoNotes Release 5.0 LDC2013T19. Web Download. Philadelphia: Linguistic Data Consortium, 2013. 

Asahi Ushio and Jose Camacho-Collados. 2021. TNER: An all-round Python library for transformer based named entity recognition.  In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 53–62, Online. Association for Computational Linguistics.

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Comments (2)

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sbaloni_geosaurus Item Owner commented 2 months ago Delete Reply

Hi @luxgeomatique , This model is expected to work with languages other than English, including Mandarin Chinese, Arabic, French, German, etc.

luxgeomatique Item Owner commented 2 years ago Delete Reply

Does is also work for other languages than English?

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          NER, LivingAtlasDLPK, Entity Recognition, nlp

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          T-NER: a python tool for language model finetuning on named-entity-recognition (NER) implemented in pytorch.

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