Wednesday, August 31, 2011
8/30/2011 class 2nd tweet
8/30/2011 class tweet
8/30/2011
* in DB the query result must have 100% Precision and 100% Recall whereas in IR it has to be a balance of the two to be acceptable.
* all the results of DB query any addition or removal of entries from the result set would make the result incorrect, whereas in IR to overcome the errors in result set it generates ranking to represent relevance of each entry.
8/30/2011
08/25/2011
08/25/2011
- Anybody can put up information on web. Nobody authenticates or approves that information
- We can gain correct knowledge only if enough people put up correct information on the web
08/30/2011
- Area under the curve is the best measure of precision and recall.
- For an ideal system, the area under the curve is maximum (precision = 1 for all values of recall, area under the curve = 1*1 = 1 )
08/30/2011
08/30/2011
Avinash Bhashyam
1202913681
Graduate Student,
Master of Science Computer Science,
Arizona State University
Phone - (312) 810-2690
Tuesday, August 30, 2011
8/30/2011
Precision = tp/(tp+fp)
Recall = tp/(tp+fn)
tp- Relevant documents retrieved by the system
fp- All documents retrieved by the system, based on the user query
fn – All the relevant documents present
Precision/recall values can be plotted and the area under the Precision/recall curve is a measure of the effectiveness.
Practically, 100% effectiveness is almost impossible to achieve. Why? Because, it is entirely dependent on the User.
Ramya
08/30/2011
some combination of the prior. True relevancy is ultimately decided by
the consumer/user/evaluator, however this can be extremely difficult
to establish. Unlike information retrieval which returns imperfect
results, data retrieval should always return perfect results.
Additional environmental information such as previous queries used,
previous results, information about the user, etc, can all be used to
better improve relevancy. -Thomas Hayden
8/30/2011
Keywords are a weak projection of what users want and search engine is expected to give the data. Imprecise queries are IR's hardest problem. The true relevance of the resultset is decided by the user.
Archana
8/30/2011
8/30/2011
Precision = tp/(tp+fp)
Recall = tp/(tp+fn)
tp- set of documents required by/sent to the user by the search system
fp- Documents user does not need but returned to the user
fn – set of documents that user wants but not sent by the system
Precision is something we can tell that we missed it unlike recall
Srividya
8/30/2011
8/30/2011
->Information retrieval consists of interpreting (implicit) the documents and ranking them in accordance to the relevance of user query.
-Dinu
08/30/2011
The weighted harmonic mean of precision and recall is called the F-measure or balanced F-score and is given by:
Fwd: 8/30/2011
8/30/2011
8/30/2011
August 30th, 2011
8/25/2011
Thursday, August 25, 2011
8/25/2011
8/25/2011
Srividya