Learn how to use different types of NLP entities to extract phone numbers from text. Find out which entity is best suited for this task and why.
Table of Contents
Question
Which type of natural language processing (NLP) entity is used to identify a phone number?
A. regular expression
B. machine-learned
C. list
D. Pattern.any
Answer
A. regular expression
Explanation
The correct answer is A. regular expression.
According to the documentation, a regular expression is a type of natural language processing (NLP) entity that is used to identify patterns of text that follow a specific format, such as phone numbers, email addresses, dates, or URLs. A regular expression entity is defined by a sequence of characters that specify the rules for matching the text, such as digits, letters, symbols, or operators. For example, the regular expression \d{3}-\d{3}-\d{4} can be used to match a phone number in the format of 123-456-7890.
A machine-learned entity is a type of NLP entity that is used to identify concepts or categories of text that are not predefined, such as person names, product names, or locations. A machine-learned entity is defined by a set of example utterances and labels that are used to train a machine learning model to recognize the entity in new utterances. For example, the machine-learned entity PersonName can be trained with utterances like “My name is Alice” or “I met Bob yesterday” and labels like PersonName: Alice or PersonName: Bob.
A list entity is a type of NLP entity that is used to identify text that belongs to a predefined list of values, such as colors, days of the week, or countries. A list entity is defined by a set of synonyms or variations for each value in the list. For example, the list entity Color can be defined with values like Red, Green, Blue, and synonyms like Crimson, Emerald, Azure.
A Pattern.any entity is a type of NLP entity that is used to identify text that does not follow any specific format or category, but rather matches a general pattern in an utterance. A Pattern.any entity is defined by a phrase list that contains words or phrases that are related to the entity. For example, the Pattern.any entity Food can be defined with a phrase list that contains words like pizza, burger, salad, sushi, and so on.
Reference
- Entity | Cloud Natural Language API | Google Cloud
- Introduction to Entity Extraction: What Is It And How It Works (monkeylearn.com)
- Named Entity Recognition | Guide to Master NLP (Part 10) (analyticsvidhya.com)
- What is Named Entity Recognition (NER) : Definition, Examples, Types, and Applications (shaip.com)
- The 2022 Definitive Guide to Natural Language Processing (NLP) (nexocode.com)
- What is Natural Language Processing? | IBM
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