Keyword-based Information Retrieval (IR) Task

A Keyword-based Information Retrieval (IR) Task is an IR task that retrieves relevant Information Items based on matching Keywords within an IR Query to terms in a corpus or dataset.

• Context:
  • It can (typically) involve an Unstructured Data Source such as a Text Corpus or a Document Repository.
  • It can (often) use Boolean Search Operators to refine the query, such as AND, OR, and NOT for keyword combinations.
  • It can range from a simple Single-Keyword Retrieval to a complex Multi-Keyword Boolean Query.
  • It can leverage Inverted Index structures to efficiently locate Keywords in large datasets.
  • It can measure performance using Precision and Recall metrics, focusing on how well the system returns relevant documents based on Keyword matches.
  • It can be applied to tasks such as Web Search, Digital Library Search, or Enterprise Search using Keywords.
  • It can rely on a user-specified Query String or automatically generated Keyword Sets, such as those from Automatic Query Expansion.
  • It can be enhanced by Relevance Feedback, allowing the system to refine Keyword matches based on user interactions.
  • It can be implemented in systems using classic IR techniques like TF-IDF or advanced models like BM25 for matching Keywords.
  • It can handle variations in Keyword phrasing using techniques like Stemming or Lemmatization.
  • ...
• Example(s):
  • a Keyword-based Web Search Task using Keywords such as "Best Smartphone 2024" that retrieves relevant webpages.
  • a Keyword-based Digital Library Search Task where a researcher searches for "Climate Change Impact" using relevant Keywords to find scientific papers.
  • an Keyword-based Enterprise Search Task where an employee searches for internal documents using specific Keywords like "Quarterly Report 2023".
  • a Keyword-based Legal Document Retrieval Task that finds cases based on Keyword searches like "Contract Breach Case Law".
  • ...
• Counter-Example(s):
  • a Natural Language Query Retrieval Task, which interprets queries based on the meaning of the entire sentence rather than individual Keywords.
  • a Semantic Search Task, which focuses on the contextual meaning of words rather than exact Keyword matches.
  • a Topic Modeling Task, which clusters documents by latent topics rather than by Keyword-based relevance.
• See: Boolean Search, Inverted Index, Relevance Feedback, TF-IDF, Web Search, Enterprise Search, Full-Text Search.

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References

2024

• (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Information_retrieval Retrieved:2024-9-16.
  • Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

    Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications.

  • NOTES:
    • Keyword IR starts when a user enters a query, typically composed of specific keywords, into an Information Retrieval System.
    • Keyword IR relies on matching user-specified or automatically generated keywords against the content of a corpus or Dataset.
    • Keyword IR often uses Boolean search operators like AND, OR, and NOT to refine the query and expand or limit search results.
    • Keyword IR typically ranks results based on relevance to the keywords provided in the query, with higher-ranked items shown first.
    • Keyword IR is most commonly used in applications like web search engines, digital libraries, and Enterprise Search systems.
    • Keyword IR often uses an Inverted Index to efficiently retrieve documents containing the specified keywords in large datasets.
    • Keyword IR may utilize TF-IDF or BM25 to score the relevance of documents based on the importance and frequency of the keywords within the dataset.
    • Keyword IR can be enhanced by techniques like Query Expansion, which automatically adds related keywords to improve the quality of search results.
    • Keyword IR frequently uses evaluation metrics like precision, recall, and F-score to measure how accurately the system retrieves relevant documents based on the given keywords.
    • Keyword IR can be applied to different media, including text documents, images, audio files, and videos, though text-based keyword searches are most common.
    • Keyword IR is commonly associated with combating Information Overload by allowing users to find relevant data quickly and efficiently from large datasets.

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