6 Relevance feedback and query expansion
Query refining
Synonymy has an impact on the recall of most information retrieval systems
independent of the query
dependent on the query
Two types
Global methods
Expanding or reformulating query terms independent of the query
Local methods
Adjusting a query relative to the documents that initially appear to match the query
The types of Global methods
Query expansion/reformulation with a thesaurus or WordNet
Query expansion via automatic thesaurus generation
Techniques like spelling correction
The type of Local methods
Relevance feedback
Pseudo-relevance feedback, also known as Blind relevance feedback
(Global) indirect relevance feedback
6.1 Relevance feedback
Relevance feedback is one of the most used and most successful approaches
The base idea
It may be difficult to formulate a good query when you don’t know the collection well
Seeing some documents may lead users to refine their understanding of the information they are seeking
The approach of RF
The user issues a (short, simple) query
The system returns an initial set of retrieval results
The user marks some returned documents as relevant or not relevant
The system computes a better representation of the information need based on the user feedback
The system displays a revised set of retrieval results
An example of RF
Image search provides a good example of relevance feedback, which is a domain where a user can easily have difficulty formulating what they want in words, but can easily indicate relevant or nonrelevant images
http://nayana.ece.ucsb.edu/imsearch/imsearch.html
Instructions
Browse: If the first page displayed doesn't include any interesting images, click browse to see the next page
Search: Once you find some initial images you are interested, click on them to select and press search
Iterate: After the search results are displayed, select/unselect more relevant images and click search
The system is based on relevance feedback and it learns while you select more images and iterate
6.1.1 The Rocchio algorithm for RF
The classic algorithm
The models based on VSM
Relevance feedback can improve both recall and precision
But, in practice, it has been shown to be most useful for increasing recall
The underlying theory
We want to find a query vector, that maximizes similarity with relevant documents while minimizing similarity with nonrelevant documents
If Cr is the set of relevant documents and Cnr is the set of nonrelevant documents, then we wish to find:
Under cosine similarity, the optimal query vector for separating the relevant and nonrelevant documents is:
The optimal query is the vector difference between the centroids of the relevant and nonrelevant documents
The key is getting the full set of relevant documents and the full set of nonrelevant documents based on users’ feedback
Rocchio algorithm
此主题相关图片如下：
q0 is the original query vector
Dr and Dnr are the set of known relevant and nonrelevant documents respectively
α,β and γ are weights attached to each term
Rocchio algorithm
Positive feedback also turns out to be much more valuable than negative feedback, and so most IR systems set γ < β
Reasonable values might be α = 1, β = 0.75, and γ = 0.15
Ide dec-hi
Another alternative is to use only the marked nonrelevant document, which |Dnr| = 1, which has the most consistent perform
The first assumption of RF
The user has to have sufficient knowledge to be able to make an initial query which is at least somewhere close to the documents they desire
Cases where relevance feedback alone is not sufficient include:
Misspellings
Mismatch of searcher’s vocabulary versus collection vocabulary
Laptop VS. notebook computer
Cross-language information retrieval
Documents in the same language cluster more closely together
The second assumption of RF
The term distribution in all relevant documents will be similar to that in the documents marked by the users
The term distribution in all nonrelevant documents will be different from those in relevant documents
This approach does not work well if the relevant documents are a multimodal class
Subsets of the documents using different vocabulary, such as Burma vs. Myanmar
A query for which the answer set is inherently disjunctive, such as Pop stars who once worked at Burger King
Instances of a general concept, which often appear as a disjunction of more specific concepts
for example, felines
Good editorial content in the collection can often provide a solution to this problem
For example, an article on the attitudes of different groups to the situation in Burma could introduce the terminology used by different parties thus linking many document clusters
Google Sets
http://labs.google.com/sets
6.1.2 The disadvantage of RF
Not necessarily popular with users
Users are often reluctant to provide explicit feedback
User do not wish to prolong the search interaction in general
The long queries that are generated by RF are inefficient for a typical IR system
only reweight certain prominent terms in the relevant documents, such as perhaps the top 20 terms by term frequency
RF is mainly a recall enhancing strategy, and web search users are only rarely concerned with getting sufficient recall
6.1.3 Relevance feedback on the web
Similar/related pages feature
Query expansion
Excite web search engine
Clickstream mining for providing indirect RF
Web link structure analysis for providing indirect RF
But provided by page authors rather than readers
Excite web search engine, which initially provided full relevance feedback
For people who used relevance feedback, results were improved about two thirds of the time
However, the feature was in time dropped, due to lack of use
Only about 4% of user query sessions used the relevance feedback option, and these were usually exploiting the “More like this” link next to each result
About 70% of users only looked at the first page of results and did not pursue things any further
6.1.4 Evaluation of RF strategies
Empirically, one round of relevance feedback is often very useful
Two rounds is sometimes marginally more useful
Accordingly, having at east five judged documents is recommended
Evaluation methods
Compute a precision-recall graph
But unfortunately it is cheating
Fairness demands that we should only evaluate with respect to documents not seen by the user
Evaluate based on documents in the residual collection
Unfortunately, the measured performance can then often be lower than for the original query, particularly if there are few relevant documents
It is difficult to validly compare performance with and without RF because the collection size and the number of relevant documents changes
A third method is to have two collections
One is used for the initial query and relevance judgments
The second is then used for comparative evaluation
The best evaluation method is to do user studies
6.1.5 Pseudo-relevance feedback
Also known as blind relevance feedback
The advantage
Assume that the top k ranked documents are relevant, and finally to do RF as before
Evidence suggests that it tends to work better than global analysis( improve about 10%)
The disadvantage
Topic drift
For example, if the query is about copper mines and the top several documents are all about mines in Chile, then there may be query drift in the direction of documents on Chile
6.1.6 Indirect relevance feedback
Less reliable than explicit feedback
More feasible and easy to collect information
DirectHit introduced the idea of ranking more highly documents that users chose to look at more often
6.2 Global methods for query reformulation
6.2.1 Vocabulary tools for query reformulation
By means of a thesaurus or a controlled vocabulary
This includes information about
Words that were omitted from the query
Words that were stemmed to
The number of hits on each term or phrase
Whether words were dynamically turned into phrases
6.2.2 Query expansion
Users give additional input on query words or phrases, which possibly suggested by the IR system
The key is building a thesaurus for query expansion
Use of a controlled vocabulary that is maintained by human editors
Library of Congress Subject Headings
The Dewey Decimal system
An automatically derived thesaurus with word co-occurrence statistics
Query reformulations based on query log mining
Sogou vocabulary
The advantage
Not requiring any user input
Some system can do automatic query expansion with thesaurus
In PubMed system, neoplasms was added to a search for cancer automaticly
Increases recall
Widely used in many science and engineering fields
6.2.3 Automatic thesaurus generation
Automatically generated thesaurus
Methods
Exploit word cooccurrence with text statistics to find the most similar words
Feasible and common
Use a grammatical analysis of the text and to exploit grammatical relations or grammatical dependencies
Advanced but complicated
Computation of co-occurrence thesaurus
We begin with a term-document matrix A, where each cell At,d is a weighted count wt,d for term t and document d
If we then calculate C = AAT, then Cu,v is a similarity score between terms u and v, with a larger number being better
The disadvantages
Tremendous computation
Require dimensionality reduction via Latent Semantic Indexing
Require domain specific thesaurus
The quality of the associations
Term ambiguity easily introduces irrelevant statistically correlated terms
Apple computer may expand to Apple red fruit computer
Not retrieve many additional documents
Since the terms in the automatic thesaurus are highly correlated in documents anyway
The comparison to RF methods
Query expansion is less successful than relevance feedback
Query expansion may be as good as pseudo-relevance feedback
Query expansion have the advantage of being much more understandable to the system user
[此贴子已经被作者于2010-12-14 09:21:12编辑过]
加好友 
发短信
管理员
Post By：2008/4/27 7:27:12 [只看该作者]
