Data mining and Named-entity recognition

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between Data mining and Named-entity recognition

Data mining vs. Named-entity recognition

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

Similarities between Data mining and Named-entity recognition

Data mining and Named-entity recognition have 8 things in common (in Unionpedia): Bioinformatics, General Architecture for Text Engineering, Information extraction, Java (programming language), Machine learning, Natural language processing, Statistical classification, Statistical model.

Bioinformatics

Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data.

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General Architecture for Text Engineering

General Architecture for Text Engineering or GATE is a Java suite of tools originally developed at the University of Sheffield beginning in 1995 and now used worldwide by a wide community of scientists, companies, teachers and students for many natural language processing tasks, including information extraction in many languages.

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Information extraction

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents.

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Java (programming language)

Java is a general-purpose computer-programming language that is concurrent, class-based, object-oriented, and specifically designed to have as few implementation dependencies as possible.

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Machine learning

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

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Natural language processing

Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

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Statistical classification

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

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Statistical model

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of some sample data and similar data from a larger population.

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The list above answers the following questions

What Data mining and Named-entity recognition have in common
What are the similarities between Data mining and Named-entity recognition

Data mining and Named-entity recognition Comparison

Data mining has 187 relations, while Named-entity recognition has 57. As they have in common 8, the Jaccard index is 3.28% = 8 / (187 + 57).

References

This article shows the relationship between Data mining and Named-entity recognition. To access each article from which the information was extracted, please visit:

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