  Academic search engines allow scientists to explore related work relevant to
a given query. Often, the user is also aware of the "aspect" to retrieve a
relevant document. In such cases, existing search engines can be used by
expanding the query with terms describing that aspect. However, this approach
does not guarantee good results since plain keyword matches do not always imply
relevance. To address this issue, we define and solve a novel academic search
task, called "aspect-based retrieval", which allows the user to specify the
aspect along with the query to retrieve a ranked list of relevant documents.
The primary idea is to estimate a language model for the aspect as well as the
query using a domain-specific knowledge base and use a mixture of the two to
determine the relevance of the article. Our evaluation of the results over the
Open Research Corpus dataset shows that our method outperforms keyword-based
expansion of query with aspect with and without relevance feedback.
