Notes： Improving the effectiveness of information retrieval with local context analysis

Problem: the automatic expansion for the queries, which is better: the global or local method

Technique for automatic expansion:
1.  term clustering
2. local: documents based on the top ranked retrieved from the query
Effect: the local feedback can improve the retrieved results
3. new method: local context analysis: concurrences analysis techniques
use the top-ranked documents for expansion

Global Techniques:
1. term clustering: group the terms based on their concurrence
problem: it can not solve the ambiguous terms  
2. Dimensional Reduction:
3. Phrasefinder: so far the most successful skills,

Local Techniques:
1. local feedback
2. Local context analysis:
The most critical function of a local feedback algorithm is to separate terms in the top-ranked relevant documents from those in top-ranked nonrelevant documents.

The most frequent terms (except stopwords) in the top-ranked documents are used for query expansion.

HYPOTHESIS. A common term from the top-ranked relevant documents will tend to cooccur with all query terms within the top-ranked documents.

Method:
1. Build Concurrence Metrics
2. Combining the degrees of cooccurrence with all query terms
3. Differentiating rare and common query terms


 problems [Ponte 1998].
In summary, local context analysis takes these steps to expand a query Q on a collection C:
(1) Perform an initial retrieval on C to get the top-ranked set S for Q.
(2) Rank the concepts in the top-ranked set using the formula f~c, Q!.
(3) Add the best k concepts to Q.
Figure 1 shows an example query expanded by local context analysis

The application for the local context analysis:
1. The cross language retrieval
2. The topic segmentation
3. Distribution of IR, choosing the right collections to search for

The experimental conclusion: