Who should I cite: learning literature search models from citation behavior

Scientists depend on literature search to find prior work that is relevant to their research ideas. We introduce a retrieval model for literature search that incorporates a wide variety of factors important to researchers, and learns the weights of each of these factors by observing citation patterns. We introduce features like topical similarity and author behavioral patterns, and combine these with features from related work like citation count and recency of publication. We present an iterative process for learning weights for these features that alternates between retrieving articles with the current retrieval model, and updating model weights by training a supervised classifier on these articles. We propose a new task for evaluating the resulting retrieval models, where the l system takes only an abstract as its input and must produce as output the list of references at the end of the ’s article. We evaluate our model on a collection of journal, conference and workshop articl...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Steven Bethard, Dan Jurafsky
• Keywords: CIKM 2010, Citation Patterns, Information Technology, Literature Search, Retrieval Models