Information Retrieval

Information Retrieval

Information Retrieval is about quickly finding materials in a large collection of unstructured data

• IR is theories, principles and algorithms to find relevant information for a collection of unstructured data - usually text data

Information Retrieval System

IR system:

• A user of an Information Retrieval system has some information need which he wants to satisfy by sending it a query
• The system returns a list of results ranked by relevance
• 

So,

• general goal of IR systems: rank relevant items much higher than non-relevant
• to do it, the items must be scored, and it’s done with a retrieval function

IR Problem

Problem:

• we have a large data collection of unstructured data
• user interacts with the collection by sending a query
• we find some results, but in what order should they be presented?

The ‘‘central problem’’ of IR is ranking elements of the collection according to relevance for a user query

Text IR:

• Most common usecase for IR systems is finding something in textual data
• the ‘'’goal’’’ of textual IR system is to retrieve most relevant documents for the user

Basic formulation:

• Given a collection $C = {d_1, d_2, \ … \ , d_N }$ and a query $q \in Q$
• IR system given $q$ returns a ranked list of ‘‘relevant’’ documents from $C$
• documents are ‘‘relevant’’ when they contain information that the user looks for e.g. they contain the answer to the query

Relevancy

'’Retrieval function’’ is a scoring function that’s used to rank documents

• retrieval function is based on a retrieval model
• Retrieval Model defines the notion of relevance and makes it possible to rank the documents

Information Retrieval Models:

• the model should be able to represent both objects in the collection and the queries
• for documents the most popular one is Vector Space Model
• but there are other Information Retrieval Models: e.g. Probabilistic Retrieval Model

Given the model we define a retrieval function $s$

• $s: Q \times C \to \mathbb R$
• it takes a query and and a document form $C$ and returns a rank value: some real number
• we can apply $s$ to all documents in $C$ to rank them

Which documents are relevant?

• in the Vector Space Model
• assumption: relevance of a document $d$ w.r.t. to a query $q$ correlated with similarity between query and document:
• So can use some similarity function $\text{similarity}(d, q)$ to find if a document is relevant
• so the retrieval function can be a similarity function, and you’ll just need to sort all documents in $C$ by their similarity to $q$

Retrieval Process

• source: Information Retrieval (UFRT) L01-introduction.pdf

The retrieval consists of several phases:

• offline:
  • collection preparation
  • parse documents
  • build Inverted Index: index the documents so they can be retrieved faster
• online:
  • query preparation (parse the query)
  • find relevant documents and rand them according to the relevance score

Plus, the ranking may account user’s preferences:

• To make the results better
• To personalize the output
• Also we may want to account for user feedback

Evaluation

How to evaluate an IR system?

• use an IR test collection which consists of:
• documents
• queries
• and know relevance of each query for each document

Given a test collection, the quality of an IR system is evaluated with:

• Precision and Recall
• Precision: % of relevant documents in the result
• Recall: % of retrieved relevant documents

Definitions:

• If $X \subseteq C$ is the output of the IR system and $Y \subseteq C$ is the list of all relevant documents
• then $P = \cfrac

Document Clustering for IR

Ranked list is not the only way of presenting the retrieval results to the user

• the results can also be clustered
• so we want to present internal relationships between documents to IR user when outputting the result
• Scatter/Gather, a variant of K-Means for documents - first popular IR clustering technique, used for clustering web search results

There are two ways to cluster the results:

• search-result clustering (post-retrieval document clustering)
• pre-retrieval document clustering

Pre-retrieval

• Lingo: use Matrix Decomposition techniques to produce low-dimensional basis for the document space
• these base vectors can be interpreted as semantic vectors of the space
• find clustering using SVD or Non-Negative Matrix Factorization

Sources

• Information Retrieval (UFRT)
• Zhai, ChengXiang. “Statistical language models for information retrieval.” 2008.
• http://datascience.stackexchange.com/questions/1106/how-to-build-a-textual-search-engine