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Using medical domain language models for expert and non-expert language for user oriented information retrieval

Goal

Create language models for medical experts and for non-expert from Swedish medical documents and use these in order to enhance an information retrieval system to retrieve documents on a, for the user, suitable level of expertise.

Background

When searching for documents on a medical topic health care professionals and lay persons most likely are interested in finding documents on different levels of expertise. Most information retrieval systems do not adjust the returned ranked list of documents to the users background.

As Språkbanken has a Swedish medical test collection with documents marked for target group: Doctors or Patients this could be used to make language models for the two user groups which then could be used to adjust the results to the users needs.

Problem description

The approach is to make language models for medical expert language and for lay persons. The objective is to describe differences between the sublanguages and to use these models to retrieve documents suited for the user.

Recommended skills

General knowledge in Swedish.

Some knowledge of information retrieval.

Some knowledge of machine learning.

Programming skills, for example in Python.

Supervisors

Karin Friberg Heppin and possibly others from Språkbanken.

References

Hiemstra, D. 2000. Using language models for information retrieval.<http://wwwhome.cs.utwente.nl/~hiemstra/papers/thesis.pdf>

Friberg Heppin. 2010.Resolving power of search keys in MedEval – A Swedish medical test collection with user groups: Doctors and Patients. <https://gupea.ub.gu.se/handle/2077/22709>

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Page updated: 2015-11-17 09:59

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