13: Natural Language Processing (NLP)
Natural Language Processing (NLP) enables machines to interact with human language in meaningful ways. It powers applications like chatbots, translation, and sentiment analysis.
13.1 Core Concepts in NLP
13.1.1 Syntax
Syntax refers to the structure of language, governing how words are arranged to form valid sentences. Example: "The cat sat on the mat" follows English syntax rules.
13.1.2 Semantics
Semantics deals with the meaning of words and sentences. Example: "The sky is blue" conveys information about the color of the sky.
13.1.3 Pragmatics
Pragmatics focuses on the context and intended meaning behind language use. Example: "Can you open the window?" is often a polite request, not just a question.
13.2 NLP Pipeline
The NLP pipeline processes text or speech into a form machines can analyze.
Step 1: Tokenization
Breaking text into smaller units like words or sentences. Example: "I love AI" becomes ["I", "love", "AI"].
Step 2: Part-of-Speech (POS) Tagging
Identifying the grammatical role of each word (e.g., noun, verb). Example:
"I": Pronoun.
"love": Verb.
"AI": Noun.
Step 3: Parsing
Analyzing sentence structure to identify relationships between words. Example: "The cat sat on the mat" reveals that "cat" is the subject and "sat" is the verb.
Step 4: Named Entity Recognition (NER)
Identifying specific entities in text, such as names, dates, or locations. Example: "Barack Obama was born in Hawaii" identifies "Barack Obama" as a person and "Hawaii" as a location.
Step 5: Sentiment Analysis
Determining the emotional tone of text. Example:
"I love this movie" is positive.
"I hate waiting in line" is negative.
13.3 Key Techniques in NLP
13.3.1 Bag of Words (BoW)
Represents text as a collection of words, ignoring grammar and word order. Useful for basic text classification. Example: "I love AI" and "AI love I" are treated identically.
13.3.2 TF-IDF (Term Frequency-Inverse Document Frequency)
Ranks words based on importance in a document relative to a collection of documents. Example: Common words like "the" are downweighted, while unique words like "blockchain" are emphasized.
13.3.3 Word Embeddings
Transforms words into dense numerical vectors that capture semantic relationships. Popular Models:
Word2Vec.
GloVe. Example: The words "king" and "queen" are numerically close in vector space.
13.4 Deep Learning in NLP
Deep learning has revolutionized NLP by enabling systems to understand complex patterns in language.
13.4.1 Recurrent Neural Networks (RNNs)
RNNs are used for sequence data, like text, allowing the model to account for context. Example: Predicting the next word in a sentence.
13.4.2 Transformer Models
Transformers process text in parallel, significantly improving performance on tasks like translation and text generation. Popular Models:
BERT (Bidirectional Encoder Representations from Transformers).
GPT (Generative Pre-trained Transformer).
13.5 Applications of NLP
13.5.1 Chatbots
AI-powered conversational agents capable of answering queries and providing support. Example: Customer service chatbots.
13.5.2 Machine Translation
Translating text from one language to another. Example: Google Translate.
13.5.3 Sentiment Analysis
Analyzing public opinion by determining the sentiment of social media posts, reviews, or news articles. Example: Assessing customer satisfaction based on feedback.
13.5.4 Summarization
Automatically generating concise summaries of lengthy documents. Example: Summarizing news articles.
13.6 Challenges in NLP
13.6.1 Ambiguity
Words and sentences can have multiple meanings. Example: "The bank" could refer to a financial institution or a riverbank.
13.6.2 Context Dependence
Understanding depends on context, which machines may struggle to infer. Example: "She saw her duck" could mean observing a bird or crouching down.
13.6.3 Low-Resource Languages
Developing NLP models for languages with limited data is challenging. Example: Indigenous languages with few written resources.
13.7 Summary
In this chapter, we explored:
Core concepts in NLP, including syntax, semantics, and pragmatics.
The NLP pipeline, from tokenization to sentiment analysis.
Key techniques like Bag of Words, TF-IDF, and word embeddings.
The impact of deep learning, especially RNNs and transformers.
Real-world applications, such as chatbots and machine translation.
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