Named-entity recognition

At ner.systems, our mission is to provide a powerful and intuitive SaaS platform for named-entity recognition. Our software is designed to accurately identify entities and taxonomies from text, making it easier for businesses and organizations to extract valuable insights and make informed decisions. With our cutting-edge technology and commitment to innovation, we strive to be the leading provider of NER solutions for businesses of all sizes.

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NER.systems Cheat Sheet

Welcome to NER.systems, a powerful SaaS tool for named-entity recognition. This cheat sheet will provide you with everything you need to know to get started with NER.systems, including key concepts, topics, and categories related to named-entity recognition.

Key Concepts

Named-Entity Recognition

Named-entity recognition (NER) is the process of identifying and classifying named entities in text. Named entities can include people, organizations, locations, dates, times, and more. NER is an important task in natural language processing (NLP) and is used in a variety of applications, including search engines, chatbots, and more.

Taxonomy

A taxonomy is a hierarchical classification system used to organize and categorize information. In the context of NER, a taxonomy can be used to classify named entities into different categories, such as people, organizations, and locations.

Machine Learning

Machine learning is a type of artificial intelligence (AI) that allows computers to learn from data and improve their performance over time. Machine learning algorithms can be used to train models for NER, allowing them to recognize named entities more accurately.

Natural Language Processing

Natural language processing (NLP) is a field of computer science that focuses on the interaction between computers and human language. NLP techniques can be used to analyze and understand text, including named-entity recognition.

Topics

Data Preparation

Before you can perform NER on text, you need to prepare your data. This can involve cleaning and preprocessing the text, as well as annotating it with named-entity labels. NER.systems provides tools for data preparation, including data cleaning and annotation.

Model Training

To perform NER, you need to train a model that can recognize named entities in text. NER.systems provides tools for model training, including machine learning algorithms and pre-trained models.

Evaluation

Once you have trained a model, you need to evaluate its performance. NER.systems provides tools for evaluating models, including metrics such as precision, recall, and F1 score.

Deployment

After you have trained and evaluated a model, you can deploy it to perform NER on new text. NER.systems provides tools for deploying models, including APIs and integrations with other software.

Categories

People

Named entities that refer to people, such as names, titles, and pronouns.

Organizations

Named entities that refer to organizations, such as company names, government agencies, and non-profit organizations.

Locations

Named entities that refer to locations, such as cities, countries, and landmarks.

Dates and Times

Named entities that refer to dates and times, such as specific dates, times of day, and durations.

Products

Named entities that refer to products, such as brand names, model numbers, and product categories.

Events

Named entities that refer to events, such as conferences, concerts, and sporting events.

Miscellaneous

Named entities that do not fit into any of the other categories, such as numbers, email addresses, and URLs.

Conclusion

Named-entity recognition is a powerful tool for analyzing and understanding text. With NER.systems, you can easily perform NER on your own text data, using machine learning algorithms and pre-trained models. Whether you are working with people, organizations, locations, or any other type of named entity, NER.systems has the tools you need to get the job done.

Common Terms, Definitions and Jargon

1. Named-Entity Recognition (NER): A process of identifying and categorizing named entities in text.
2. Entity: A person, place, organization, or thing that is referred to in text.
3. Taxonomy: A hierarchical classification system used to organize entities into categories.
4. Natural Language Processing (NLP): A field of computer science that focuses on the interaction between computers and human language.
5. Machine Learning: A type of artificial intelligence that allows computers to learn from data and improve their performance over time.
6. Deep Learning: A subset of machine learning that uses neural networks to learn from data.
7. Neural Network: A type of machine learning algorithm that is modeled after the structure of the human brain.
8. Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn like humans.
9. Text Mining: The process of extracting useful information from text data.
10. Information Extraction: The process of automatically extracting structured information from unstructured data.
11. Named Entity Disambiguation (NED): The process of resolving ambiguous named entities in text.
12. Entity Linking: The process of linking named entities in text to their corresponding entries in a knowledge base.
13. Knowledge Base: A structured database of information that can be used to support NER and other natural language processing tasks.
14. Corpus: A collection of text documents used for training and testing NER models.
15. Part-of-Speech (POS) Tagging: The process of labeling words in text with their grammatical parts of speech.
16. Chunking: The process of grouping words in text into meaningful phrases.
17. Named Entity Recognition Evaluation (NERE): The process of evaluating the performance of NER models.
18. Precision: The proportion of correctly identified entities out of all identified entities.
19. Recall: The proportion of correctly identified entities out of all entities that should have been identified.
20. F1 Score: A measure of the overall performance of NER models that takes into account both precision and recall.

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