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Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.

Named entity recognition definition:

Named Entity Recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. Here is a simple Python program for NER using Spacy, a popular library for Natural Language Processing (NLP).

An example of named entity recognition in Python:

Please note that you need to have the necessary Python libraries installed in your Python environment to run the following code examples.

To run this example, you'll need to install the spacy library and download a language model. You can do this using pip:

pip install spacy
python -m spacy download en_core_web_sm

Now run the Python code:

In this example, we first import the spacy library and load the English language model. We then process a text string with the nlp function, which returns a Doc object that holds all information about the tokens, their linguistic features and their relationships.

We print out all noun phrases and verbs in the text, and then iterate over the ents property of the Doc object, which gives us the named entities. For each entity, we print its text and its label.

Please note that the model may not be perfect and might not catch all named entities correctly, especially with informal language, slang, or typos.

import spacy

# Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load("en_core_web_sm")

# Process whole documents
text = ("Dagster is a new data orchestration solution for modern data engineering."
        "Unlike other solutions, Dagster provides a declarative approach to building data pipelines."
        "With Dagster, your data engineering team can speed up development cycles, "
        "observe your critical data assets, "
        "and confidently test your code so that you can push to production in full confidence."

doc = nlp(text)

# Analyze syntax
print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])

# Find named entities, phrases and concepts
for entity in doc.ents:
    print(entity.text, entity.label_)

This should yield the following output:

Noun phrases: ['Dagster', 'a new data orchestration solution', 'modern data engineering', 'other solutions', 'Dagster', 'a declarative approach', 'data pipelines', 'Dagster', 'your data engineering team', 'development cycles', 'your critical data assets', 'your code', 'you', 'production', 'full confidence']
Verbs: ['provide', 'build', 'speed', 'observe', 'test', 'push']
Dagster PERSON
Dagster PERSON
Dagster PERSON

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Data Management:


Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. store data for long-term retention and compliance.


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


Create a copy of data to protect against loss or corruption.


Select, organize and annotate data to make it more useful for analysis and modeling.


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Analyzing the number of features or attributes in the data to improve performance.


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Create an optimized data structure for fast search and retrieval.


combine data from different sources to create a unified view for analysis or reporting.


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Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


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Track data processing metrics and system health to ensure high availability and performance.


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Transform your data so it is fit-for-purpose.


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


Increasing the capacity or performance of a system to handle more data or traffic.

Schema Mapping

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Check data for completeness, accuracy, and consistency.


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