Natural Language Processing (NLP)

1. Text Preprocessing

• Tokenization:

  • Description: Tokenization is the process of breaking down text into smaller units (tokens), such as words or subwords, that can be used as input for NLP models.

  • Types:

    • Word Tokenization: Splitting text into individual words.

    • Subword Tokenization: Breaking down words into smaller meaningful units (useful for handling out-of-vocabulary words).

    • Sentence Tokenization: Splitting text into sentences.

  • Use Cases: Tokenization is used in every NLP task, such as text classification, translation, and sentiment analysis.

• Stemming:

  • Description: Stemming is the process of reducing words to their root form by stripping affixes (e.g., "running" becomes "run"). This process is typically more aggressive and may result in non-dictionary words.

  • Algorithms:

    • Porter Stemmer: A common stemming algorithm that uses a set of heuristic rules.

  • Use Cases: Text classification, search engines, and document retrieval systems.

• Lemmatization:

  • Description: Lemmatization reduces words to their base or dictionary form (lemma) while ensuring that the resulting word is valid (e.g., "better" becomes "good"). It is more accurate than stemming.

  • How It Works: Lemmatization takes into account the context of the word and its part of speech to find the correct base form.

  • Use Cases: More sophisticated NLP tasks that require precise language understanding, such as question answering and machine translation.

• Stopword Removal:

  • Description: Stopwords are common words (e.g., "and," "the," "is") that carry little meaningful information in NLP tasks. Removing them helps improve model performance by focusing on more significant words.

  • Use Cases: Text preprocessing for tasks like topic modeling and document classification.

2. Language Models

• N-grams:

  • Description: N-grams are contiguous sequences of 'n' items (words or characters) from a given text. They capture patterns and relationships between adjacent words.

  • Types:

    • Unigrams: Single words.

    • Bigrams: Pairs of consecutive words.

    • Trigrams: Sequences of three words.

  • Use Cases: Language generation, text prediction, and speech recognition.

• Bag of Words (BoW):

  • Description: BoW is a method of representing text data as a collection of words, without considering grammar or word order. Each word in the text is treated as an independent feature, and its frequency is counted.

  • How It Works: The text is converted into a sparse vector where each dimension corresponds to a specific word in the vocabulary, and the value is the word's frequency in the text.

  • Use Cases: Document classification, sentiment analysis, and spam detection.

• TF-IDF (Term Frequency-Inverse Document Frequency):

  • Description: TF-IDF is a statistical measure used to evaluate how important a word is to a document relative to a collection of documents. It balances word frequency with how common the word is across all documents.

  • How It Works: TF-IDF assigns a higher weight to words that appear frequently in a document but rarely across other documents.

  • Use Cases: Information retrieval, search engines, and text mining.

• Word Embeddings:

  • Description: Word embeddings are dense vector representations of words, capturing semantic meaning by placing similar words close together in the vector space.

  • Popular Techniques:

    • Word2Vec: Converts words into vectors based on their context.

    • GloVe (Global Vectors for Word Representation): Captures word meaning by modeling global word-word co-occurrence.

    • FastText: Improves on Word2Vec by considering subword information (useful for handling rare words).

  • Use Cases: Sentiment analysis, machine translation, and document similarity analysis.

3. Advanced NLP

• Named Entity Recognition (NER):

  • Description: NER is the process of identifying and classifying named entities (e.g., people, organizations, locations, dates) in text.

  • How It Works: The model uses contextual clues to detect words that represent entities and assigns them predefined labels.

  • Use Cases: Information extraction, question answering, and text summarization.

• Machine Translation:

  • Description: Machine translation involves automatically translating text from one language to another using NLP models.

  • How It Works: Modern translation systems like Google Translate use neural networks (Seq2Seq models with attention mechanisms) to translate text while preserving meaning and context.

  • Use Cases: Cross-language communication, document translation, and multilingual applications.

• Sentiment Analysis:

  • Description: Sentiment analysis is the process of detecting emotions, opinions, or attitudes expressed in a piece of text.

  • How It Works: The model analyzes words and phrases to categorize the sentiment of the text as positive, negative, or neutral. Deep learning models (RNNs or transformers) often outperform traditional methods.

  • Use Cases: Social media monitoring, customer feedback analysis, and product reviews.