What are the elements a benchmark dataset should have to measure the relevance of search results

List of elements a benchmark dataset should have in information retrieval task, what we need for a benchmark dataset, what do we need to measure the retrieval effectiveness of a search system, standard benchmark collection for information retrieval evaluation

Question: What are the elements a benchmark dataset should have to measure the relevance of search results?   Answer: The retrieval effectiveness of a system is evaluated on a set of documents, queries, and relevance judgments. A benchmark dataset should have the following elements; A document collection Documents must be representative of the documents we expect to see in reality A set of queries It refers to a collection of information needs. The set of queries must also be representative of the information that we need in reality. An assessment by human judges on the relevancy of documents for different information needs. We need to involve humans to judge whether a document is relevant or not for a query. It is usually a costly process. Some standard benchmark collections include Cranfield, TREC (Text Retrieval Conference), and CLEF (Cross Language Evaluation forum).

Related links/questions

• How to improve the recall of an information retrieval system

• What are the global resources for query expansion / reformulation?

• Machine learning multiple choices questions with detailed answers - home page

• List down the local methods for improving recall of IR systems

• What are the drawbacks of Boolean retrieval model?

• What are the drawbacks of relevance feedback in improving search results

• Common preprocessing steps and their significance in information retrieval

             

Keywords

List few benchmark data collection for information retrieval evaluation.

Information retrieval evaluation methods

How to measure the retrieval effectiveness of a  retrieval system

What are the problems with Jaccard similarity coefficient

List the issues with Jaccard similarity coefficient, What are the problems with Jaccard index, Define Jaccard index, Can we use Jaccard index to find similarity between two documents 

 

Question: List down the issues/problems with Jaccard similarity.   Answer: The Jaccard similarity coefficient, is a statistic used for gauging the similarity and diversity of sample sets. It is calculated as follows; Jaccard (A, B) = |A ∩ B|/|A U B| The number of common members in two sets divided by the total number of members in both the two sets is the Jaccard coefficient. It can be a value between 0 and 1 where 0 indicates no overlap and 1 indicates perfect overlap. Problems with Jaccard It doesn’t consider term frequency (how many times a term occurs in a document). It simply counts the number of terms that are common between two sets. Rare terms in a collection are more informative than frequent terms. Jaccard doesn’t consider this information. Different sized sets with same number of common members also will result in the same Jaccard similarity.

 

Related links/questions

• How to improve the recall of an information retrieval system

• What are the global resources for query expansion / reformulation?

• Machine learning multiple choices questions with detailed answers - home page

• List down the local methods for improving recall of IR systems

• What are the components of a benchmark data collection?

• What are the drawbacks of Boolean retrieval model?

• What are the drawbacks of relevance feedback in improving search results

• Common preprocessing steps and their significance in information retrieval

             

Keywords

List down the issues with Jaccad similarity coefficient

What are the disadvantages of Jaccard similarity index

What Jaccard index value gives perfect overlap?

Can we use Jaccard similarity to measures the closeness between two text documents?