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Search Systems and Findability Flashcards

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  1. Which component of a search system is responsible for transforming raw documents into indexed, searchable representations?

    Answer: Index builder

    The index builder (or indexer) processes raw content and creates the inverted index structure that enables fast retrieval.

  2. In information architecture, 'precision' in search refers to:

    Answer: The percentage of relevant documents retrieved out of all retrieved documents

    Precision measures the ratio of relevant results returned to total results returned, indicating how 'clean' the result set is.

  3. A 'stopword' in search indexing is best described as:

    Answer: A common word (e.g., 'the', 'and') excluded from the index to improve efficiency

    Stopwords are high-frequency, low-value words that are typically excluded from the index to reduce noise and storage overhead.

  4. Which search pattern best supports users who know what they want but cannot recall the exact terminology?

    Answer: Associative or 'Don't know what I need' search

    Associative search supports users with a vague or approximate understanding of their target, relying on related terms and browse paths.

  5. What is the primary purpose of query expansion in a search system?

    Answer: To broaden retrieval by adding synonyms or related terms to the original query

    Query expansion automatically adds synonyms, related terms, or alternate spellings to improve recall for users whose terms don't exactly match indexed content.

  6. In the context of faceted search, a 'facet' is most accurately defined as:

    Answer: A pre-defined dimension or category attribute used to filter search results

    A facet is a controlled attribute dimension (e.g., price range, color, format) that allows users to progressively narrow a result set.

  7. Which ranking signal is most characteristic of TF-IDF (Term Frequency-Inverse Document Frequency)?

    Answer: It scores terms higher when they appear frequently in a document but rarely across the corpus

    TF-IDF rewards terms that are frequent within a specific document but rare across the entire collection, making them strong discriminators.