Definition
In natural language processing, Part-of-Speech (POS) tagging is a technique that tags individual words within a sentence with specific grammatical categories or labels (such as nouns, verbs, adjectives, adverbs, pronouns, etc.). This process reveals insights into the syntactic structure of the text, enabling understanding of word relationships, disambiguating word meanings, and facilitating various linguistic and computational analyses of textual data [1].
The following is
a simple example of part of speech tagging;
Chioma (Proper Noun) wore (Verb) beautiful (Adjective) ankara (Noun) to (Preposition) the (Determiner) wedding (Noun) ceremony (Noun).
Universal POS Tags [1]
Origin
The origin of POS Tagging can be traced back to the 1950s, when rule-based approaches were first used to assign parts of speech to words based on their morphological and syntactic properties. Over the course of time, POS Tagging techniques have developed to include machine learning-based approaches, which have significantly improved the accuracy and efficiency of POS Tagging [2].
Context and Usage
POS Tagging is utilized by linguists, NLP engineers, data analysts, and researchers in many industries and contexts, including social media analytics, academic research, and language teaching and learning. It's essentially a secret code that enable us understand the mysteries of human language [3].
Why it Matters
Text data is a widely available problem domain for NLP tasks. Natural language processing is a branch of machine learning that deals with how machines understand human languages. This is where POS tagging comes in. It is a text preprocessing technique that makes it possible to work with text data by transforming the raw text into a form that can be understood and used by machine learning algorithms [4].
In Practice
Isahit is a real-life case study of part of speech tagging being practiced. They enhance the performance of your NLP models and optimize data analysis with their expert POS tagging services. Some of the industries they cater to include Consumer Goods and Retail, Travel and Tourism. Leveraging cutting-edge labeling tools, their skilled workforce ensures maximum accuracy and efficiency, enabling you to gain enhanced insights [5].
See Also
Related NLP and
Text Processing terms:
- Self-Supervised Learning: Learning approach that creates supervision signals from the data itself
- Semantic (AI): Relating to the meaning and interpretation of words, phrases, or symbols
- Semantic Annotation: Process of adding meaningful metadata or labels to content for better understanding
- Semantic Network: Graph structure representing knowledge through interconnected concepts and relationships
- Semantic Search: Search technique that understands meaning and context rather than just matching keywords
References
- Sharma, A. (2024). How Part-of-Speech Tag, Dependency and Constituency Parsing Aid In Understanding Text Data?
- Lee, S. (2025). Mastering Part-of-Speech Tagging Techniques
- Botpenguin. (2025). Parts-of-speech (POS) Tagging.
- Saxena, S. (2021). Parts of Speech Tag and Dependency Grammar.
- Isahit. (2025). POS tagging