Last Week Note and No Muddist

NO MUDDIST POINT FOR THE LAST WEEK, Thank you!

Generalizing from relevance feedback using named entity wildcards

This paper proposes news ways of generalizing from relevance feedback by augmenting the traditional bag-of-words query model with named entity wildcards that are anchored in context. The use of wildcards allows generalization beyond specific words, while contextual restrictions limit the wildcard-matching to entities related to the user's query. We test our new approach in a nugget-level adaptive filtering system and evaluate it in terms of both relevance and novelty of the presented information. Our results indicate that higher recall is obtained when lexical terms are generalized using wildcards. However, such wildcards must be anchored to their context to maintain good precision. How the context of a wildcard is represented and matched against a given document also plays a crucial role in the performance of the retrieval system.

Learning to rank for information retrieval

The task of "learning to rank" has emerged as an active and growing area of research both in 

information retrieval and machine learning. The goal is to design and apply methods to automatically 

learn a function from training data, such that the function can sort objects (e.g., documents) according 

to their degrees of relevance, preference, or importance as defined in a specific application.

The relevance of this task for IR is without question, because many IR problems are by nature 

ranking problems. Improved algorithms for learning ranking functions promise improved retrieval 

quality and less of a need for manual parameter adaptation. In this way, many IR technologies can be 

potentially enhanced by using learning to rank techniques.