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
- 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.
- Tokenization
- Normalization: Map term variant to the same form.
- Stemming: Extract root word.
- Stop words: Omit common words
Last modified 2yr ago