If you walk to an intersection of computational linguistics, artificial intelligence, and computer science, you are more than likely to see Natural Language Processing (NLP) there as well. NLP involves computers processing natural language—human-generated language and not math or programming languages like Java or C++.
Famous examples of NLP include Apple’s SIRI (speech recognition/generation), IBM Watson (question answering), and Google Translate (machine translation). NLP extracts meaning from human language despite its inherent ambiguity.
Recall HAL from Stanley Kubrick’s film 2001: A Space Odyssey? HAL performed information retrieval, extraction, inference, played chess, displayed graphics, and engaged in conversation—tasks that modern NLP systems like Microsoft Cortana, Palantir, and Facebook graph search now perform.
NLP consists of Natural Language Generation (NLG) and Natural Language Understanding (NLU). NLG enables computers to write like humans. NLU involves comprehending text, managing ambiguities, and producing meaningful data.
What makes up NLP?
Entity Extraction
Entity extraction identifies and segments entities such as people, places, and organizations from text. It clusters variations of the same entity.
- Entity type: Person, place, organization, etc.
- Salience: Relevance score of the entity in context (0 to 1)
For example, variations like "Roark", "Mr. Roark", and "Howard Roark" are clustered under the same entity.
Google NLP API can analyze sentences for such entities. For instance, in a paragraph about Karna from the Mahabharata, the API might assign a salience score of 0.5 to Karna.


Syntactic Analysis
Syntactic analysis checks sentence structure and parts of speech. Using parsing algorithms and dependency trees, it organizes tokens based on grammar.


Semantic Analysis
Semantic analysis interprets sentence meaning in a context-free way, often using lexical and compositional semantics.

For instance, “Karna had an apple” may be interpreted as “Karna owned an apple,” not “ate.” World knowledge is essential for true understanding.

Sentiment Analysis
Sentiment analysis identifies emotions, opinions, and attitudes—subjective content. Scores range from -1 (negative) to +1 (positive), and magnitude reflects intensity.



Pragmatic Analysis
Pragmatic analysis considers the context of utterances—who, when, where, and why—to determine meaning. For instance, “You are late” could be informative or critical.

Linguists and NLP systems approach pragmatics differently, as noted here.
A Few Applications of NLP
- AI chatbots helping with directions, bookings, and orders
- Paraphrasing tools for marketing and content creation
- Sentiment analysis for political campaigns
- Analyzing user reviews on e-commerce platforms
- Customer feedback analytics in call centers
Different APIs offer customized NLP features. Advanced NLP uses statistical machine learning and deep analytics to manage unstructured data.
Despite natural language's complexity, NLP has made impressive strides. Alan Turing would surely be proud.