Information Retrieval

This note mainly focuses on text information retrieval. It is mainly based on JHU's, ETHzurich's and Stanford's information retrieval course.


Data -> Search -> User

More detail:

  • Data: Getting documents and preprocessing documents

    • Crawler

    • Text preprocessing, Clustering, Information Extraction (Named Entity, Relation, Topic Models, etc.)

    • Forward Index, Inverted Index

  • Search: Querying content (Search engine) or filtering content (Recommendation system). Not different that much.

    • Querying: Boolean Retrieval, Vector Space Model, Probabilistic Model, Learning to Rank

    • Filtering: Content Filtering, Collaborative Filtering, Also use Querying methods

    • Ranking: Scoring, Link Analysis

  • User: Content presentation

Retrieval In General

  • The way user accessing data: Push mode (Recommendation system like news feed) -> filtering content and Pull model (Search engine) -> querying content.

  • Retrieval compared to Database: Database usually holds structured data, with well-defined query semantics.

  • Know that user information need is almost always larger than the given query.

  • Search core methods: Selection (binary decision) or Ranking (Continous scoring and thresholding). If we assume the utility of a document to a user is independent of any other document and the usesr browse the results sequentially, we could rank documents in descending order of the probability that a document is relevant to the query.

  • Search results evaluation: Precision and Recall.

General Text Preprocessing

  • Tokenization

  • Normalization: Map term variant to the same form.

  • Stemming: Extract root word.

  • Stop words: Omit common words

Main Topics

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