Natural Language Processing Overview

Natural Language Processing (NLP)

Natural Language Processing

Natural Language Processing (NLP) is a subfield of AI concerned with enabling computers to understand, interpret, and generate human language. It bridges the gap between human communication and computer understanding.

Classification of NLP tasks into syntax, semantics, discourse, and application areas.

Key Tasks in NLP

• Tokenization: Splitting text into words or subword units.

• Parsing: Analyzing grammatical structure (syntax trees).

• Named Entity Recognition (NER): Identifying entities like persons, organizations, locations.

• Sentiment Analysis: Determining the emotional tone of text.

• Machine Translation: Translating between languages (e.g., Google Translate).

• Question Answering: Answering questions posed in natural language.

• Text Summarization: Generating concise summaries of longer texts.

• Speech Recognition: Converting spoken language to text.

• Relation Extraction: Identifying relationships between entities in text.

Applications of NLP

• Chatbots and virtual assistants (Siri, Alexa, ChatGPT)

• Search engines and information retrieval

• Machine translation systems (Google Translate, DeepL)

• Spam detection and text classification

• Medical record analysis and clinical decision support

• Legal document review and contract analysis

NLP in Action

Consider the sentence: “Apple Inc. purchased a startup in Cupertino.”

NLP tasks applied:

• Tokenization: [Apple, Inc., purchased, a, startup, in, Cupertino, .]

• NER: Apple Inc. (ORG), Cupertino (LOC)

• POS Tagging: Apple (NNP), purchased (VBD), a (DT), startup (NN), in (IN), Cupertino (NNP)

• Relation Extraction: (, )

• Sentiment: Neutral (factual statement)

FOL and NLP Connection

First-order logic provides a formal representation for the meaning of natural language sentences. FOL can represent:

• Quantified statements (“All students study AI” $\rightarrow$ $\forall x \; \text{Student}(x) \Rightarrow \text{Studies}(x, \text{AI})$)

• Relationships (“John knows Mary” $\rightarrow$ $\text{Knows}(\text{John}, \text{Mary})$)

• Complex logical structure (“If it rains, the ground gets wet” $\rightarrow$ $\text{Rains} \Rightarrow \text{WetGround}$)

NLP systems often use logical forms as intermediate representations for semantic understanding. FOL-based inference enables question answering and reasoning over the extracted meaning.