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.