Concept extraction is the process of identifying and extracting concepts from the text. The extracted concepts can be used for various purposes such as building a knowledge base or ontology, information retrieval, text classification, etc.
Ways to Perform Concept Contraction
There are many ways to perform concept extraction. Some methods are rule-based while others are Statistical NLP-based. Rule-based methods involve the use of hand-crafted rules to identify and extract concepts. Statistical NLP-based methods make use of machine learning algorithms that are trained on a labeled dataset.
Concept extraction can be performed at different levels of granularity. For example, you can extract named entities such as people, places, organizations, etc. or you can extract topics and themes from the text.
Concept Extraction vs. Theme Extraction
Theme extraction is another term that is often used in the text analytics industry. Theme extraction is a specific type of concept extraction where the focus is on extracting topics and themes from the text. While concept extraction can be performed at different levels of granularity, theme extraction is usually performed at a higher level of granularity. This means that named entities are not extracted as themes or topics.
Concept extraction is a more general term that can be used to refer to any type of concept extraction, while theme extraction specifically refers to the extraction of topics and themes from text.
Concept Extraction vs. Other Similar Terms
The term concept extraction is sometimes used interchangeably with other terms such as named entity recognition (NER), information extraction (IE), or entity extraction. However, there are some important distinctions between these terms. Named entity recognition is a task that involves identifying and classifying named entities in text into predefined categories such as person, location, organization, etc. Information extraction is a broader task that involves extracting structured information from unstructured or semi-structured text. Entity extraction is a more general term that can be used to refer to either named entity recognition or information extraction. Unlike Concept extraction, it is not limited to extracting named entities or other structured information, it can also be used to extract any kind of concept from the text. For example, you could use concept extraction to extract product features from customer reviews, recipes from cooking blogs, or topics and themes from news articles.