Named Entity Recognition | Dagster Glossary

Back to Glossary Index

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

Other data engineering terms related to
Data Management:
Dagster Glossary code icon

Append

Adding or attaching new records or data items to the end of an existing dataset, database table, file, or list.
An image representing the data engineering concept of 'Append'

Archive

Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. Store data for long-term retention and compliance.
An image representing the data engineering concept of 'Archive'
Dagster Glossary code icon

Augment

Add new data or information to an existing dataset to enhance its value.
An image representing the data engineering concept of 'Augment'

Backup

Create a copy of data to protect against loss or corruption.
An image representing the data engineering concept of 'Backup'
Dagster Glossary code icon

Batch Processing

Process large volumes of data all at once in a single operation or batch.
An image representing the data engineering concept of 'Batch Processing'
Dagster Glossary code icon

Cache

Store expensive computation results so they can be reused, not recomputed.
An image representing the data engineering concept of 'Cache'
Dagster Glossary code icon

Categorize

Organizing and classifying data into different categories, groups, or segments.
An image representing the data engineering concept of 'Categorize'
Dagster Glossary code icon

Deduplicate

Identify and remove duplicate records or entries to improve data quality.
An image representing the data engineering concept of 'Deduplicate'

Deserialize

Deserialization is essentially the reverse process of serialization. See: 'Serialize'.
An image representing the data engineering concept of 'Deserialize'
Dagster Glossary code icon

Dimensionality

Analyzing the number of features or attributes in the data to improve performance.
An image representing the data engineering concept of 'Dimensionality'
Dagster Glossary code icon

Encapsulate

The bundling of data with the methods that operate on that data.
An image representing the data engineering concept of 'Encapsulate'
Dagster Glossary code icon

Enrich

Enhance data with additional information from external sources.
An image representing the data engineering concept of 'Enrich'

Export

Extract data from a system for use in another system or application.
An image representing the data engineering concept of 'Export'
Dagster Glossary code icon

Graph Theory

A powerful tool to model and understand intricate relationships within our data systems.
An image representing the data engineering concept of 'Graph Theory'
Dagster Glossary code icon

Idempotent

An operation that produces the same result each time it is performed.
An image representing the data engineering concept of 'Idempotent'
Dagster Glossary code icon

Index

Create an optimized data structure for fast search and retrieval.
An image representing the data engineering concept of 'Index'
Dagster Glossary code icon

Integrate

Combine data from different sources to create a unified view for analysis or reporting.
An image representing the data engineering concept of 'Integrate'
Dagster Glossary code icon

Lineage

Understand of how data moves through a pipeline, including its origin, transformations, dependencies, and ultimate consumption.
An image representing the data engineering concept of 'Lineage'
Dagster Glossary code icon

Linearizability

Ensure that each individual operation on a distributed system appear to occur instantaneously.
An image representing the data engineering concept of 'Linearizability'
Dagster Glossary code icon

Materialize

Executing a computation and persisting the results into storage.
An image representing the data engineering concept of 'Materialize'
Dagster Glossary code icon

Memoize

Store the results of expensive function calls and reusing them when the same inputs occur again.
An image representing the data engineering concept of 'Memoize'
Dagster Glossary code icon

Merge

Combine data from multiple datasets into a single dataset.
An image representing the data engineering concept of 'Merge'
Dagster Glossary code icon

Model

Create a conceptual representation of data objects.
An image representing the data engineering concept of 'Model'

Monitor

Track data processing metrics and system health to ensure high availability and performance.
An image representing the data engineering concept of 'Monitor'
Dagster Glossary code icon

Parse

Interpret and convert data from one format to another.
Dagster Glossary code icon

Partition

Data partitioning is a technique that data engineers and ML engineers use to divide data into smaller subsets for improved performance.
An image representing the data engineering concept of 'Partition'
Dagster Glossary code icon

Prep

Transform your data so it is fit-for-purpose.
An image representing the data engineering concept of 'Prep'
Dagster Glossary code icon

Preprocess

Transform raw data before data analysis or machine learning modeling.
Dagster Glossary code icon

Replicate

Create a copy of data for redundancy or distributed processing.

Scaling

Increasing the capacity or performance of a system to handle more data or traffic.
Dagster Glossary code icon

Schema Inference

Automatically identify the structure of a dataset.
An image representing the data engineering concept of 'Schema Inference'
Dagster Glossary code icon

Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.
Dagster Glossary code icon

Secondary Index

Improve the efficiency of data retrieval in a database or storage system.
An image representing the data engineering concept of 'Secondary Index'

Synchronize

Ensure that data in different systems or databases are in sync and up-to-date.
Dagster Glossary code icon

Validate

Check data for completeness, accuracy, and consistency.
An image representing the data engineering concept of 'Validate'
Dagster Glossary code icon

Version

Maintain a history of changes to data for auditing and tracking purposes.
An image representing the data engineering concept of 'Version'