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Tokenization is the process of splitting raw text into smaller units called tokens, which can be words, subwords, or characters. It is the first step in NLP preprocessing and directly impacts how models interpret meaning. Word tokenization: splits text by ...

A document embedding is a fixed-length vector representation of an entire text — whether a sentence, paragraph, or full page. Lexical models (BoW, TF-IDF) only capture word presence or frequency. Document embeddings encode semantic similarity between texts, allowing machines to ...

LDA is a Bayesian topic model that uncovers the latent structure of text. Instead of classifying a document into a single category, it treats every document as a mixture of multiple topics. A document might be 60% “machine learning” and ...

Latent Semantic Analysis is a mathematical technique that uses Singular Value Decomposition (SVD) to reveal hidden relationships in large text corpora. Surface Level (BoW/TF-IDF): Words are treated as independent, literal tokens. Latent Level (LSA): Words and documents are mapped into ...

Text summarization aims to condense content while preserving meaning. Two broad categories exist: Extractive Summarization: Selects important sentences directly from the source text. Abstractive Summarization: Generates new sentences to convey the same meaning in a more concise form. Extractive methods ...

Machine Translation is the process of converting text in one language into another while preserving meaning, style, and fluency. Unlike a dictionary lookup, MT must navigate: Ambiguity (words with multiple meanings). Grammar and word order differences. Morphological complexity across languages. ...

Information Extraction transforms unstructured text into structured forms, enabling downstream reasoning. It includes: Named Entity Recognition (NER): spotting entity mentions. Relationship Extraction (RE): mapping links between entities. Event Extraction: capturing actions and their participants. NER provides the nodes, while RE ...

Text classification is built on a pipeline of preprocessing, feature extraction, modeling, and evaluation. The most common features include bag-of-words and TF-IDF, which represent documents as weighted vectors of terms. This process is similar to how information retrieval systems operate: ...

A Sequence-to-Sequence (Seq2Seq) model is a neural network architecture designed to transform one sequence into another, such as translating a sentence, summarizing a document, or converting speech into text. Key components: Encoder → Reads the input sequence and compresses it ...

Traditional search models emphasize semantic similarity at the sentence or keyword level. While effective for short queries, they miss the discourse-level glue that binds meaning. Consider a paragraph: “Ali bought a new phone yesterday. It has a great camera and ...