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.

Overview

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

• Ad Hoc Retrieval

• Classification and Clustering