A document in information retrieval (IR) is a fundamental unit of data that the system processes, stores, and retrieves. It represents any self-contained piece of information, such as a text file, webpage, email, or PDF. Documents are treated as distinct entities containing content that users might search for, like keywords, phrases, or topics. For example, a webpage about weather forecasts, a research paper on machine learning, or a product description in an e-commerce database are all considered documents in IR. The key idea is that each document is indexed and made searchable based on its content, allowing users to query the system and retrieve relevant results.
In IR systems, documents undergo preprocessing to extract features for efficient retrieval. This typically involves tokenization (splitting text into words or terms), removing stop words (common words like “and” or “the”), and applying stemming or lemmatization to reduce words to their root forms. For instance, a document containing the sentence “The quick brown fox jumps” might be tokenized into ["quick", "brown", "fox", “jump”], with “the” removed and “jumps” stemmed to “jump.” These processed terms are then stored in an inverted index, a data structure that maps terms to the documents containing them. This allows the system to quickly look up which documents match a user’s query terms.
The role of documents in IR extends beyond simple storage. They form the basis for ranking algorithms like TF-IDF (term frequency-inverse document frequency) or BM25, which determine how well a document matches a query. For example, if a user searches for “machine learning algorithms,” the system might rank documents higher if they frequently mention “machine learning” and “algorithms” while avoiding those where the terms appear too commonly across all documents. Documents can also include metadata (e.g., publication date, author) or structural elements (e.g., headings in HTML), which some systems use to improve relevance. While text remains the primary focus, modern IR systems may handle multimedia documents (images, videos) by extracting textual metadata or using embeddings for similarity comparisons. Ultimately, the concept of a document enables IR systems to organize and retrieve information at scale.