TY - CONF AU - He, Qi AU - Kifer, Daniel AU - Pei, Jian AU - Mitra, Prasenjit AU - Giles, C. Lee A2 - T1 - Citation recommendation without author supervision T2 - Proceedings of the fourth ACM international conference on Web search and data mining PB - ACM C1 - New York, NY, USA PY - 2011/ CY - VL - IS - SP - 755 EP - 764 UR - http://doi.acm.org/10.1145/1935826.1935926 DO - 10.1145/1935826.1935926 KW - analysis KW - citation KW - item KW - paper KW - recommender KW - research L1 - SN - 978-1-4503-0493-1 N1 - N1 - AB - Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (without a bibliography) and our system automatically finds locations where citations are needed. We show that naïve approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective. ER - TY - CONF AU - Bethard, Steven AU - Jurafsky, Dan A2 - T1 - Who should I cite: learning literature search models from citation behavior T2 - Proceedings of the 19th ACM international conference on Information and knowledge management PB - ACM C1 - New York, NY, USA PY - 2010/ CY - VL - IS - SP - 609 EP - 618 UR - http://doi.acm.org/10.1145/1871437.1871517 DO - 10.1145/1871437.1871517 KW - analysis KW - citation KW - item KW - paper KW - recommender KW - research L1 - SN - 978-1-4503-0099-5 N1 - N1 - AB - 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 retrieval system takes only an abstract as its input and must produce as output the list of references at the end of the abstract's article. We evaluate our model on a collection of journal, conference and workshop articles from the ACL Anthology Reference Corpus. Our model achieves a mean average precision of 28.7, a 12.8 point improvement over a term similarity baseline, and a significant improvement both over models using only features from related work and over models without our iterative learning. ER - TY - CONF AU - Wang, Yonggang AU - Zhai, Ennan AU - Hu, Jianbin AU - Chen, Zhong A2 - T1 - Claper: Recommend classical papers to beginners T2 - Proceedings of the seventh International Conference on Fuzzy Systems and Knowledge Discovery PB - IEEE C1 - PY - 2010/08 CY - VL - 6 IS - SP - 2777 EP - 2781 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5569227 DO - 10.1109/FSKD.2010.5569227 KW - analysis KW - citation KW - item KW - paper KW - recommender KW - research L1 - SN - N1 - N1 - AB - Classical papers are of great help for beginners to get familiar with a new research area. However, digging them out is a difficult problem. This paper proposes Claper, a novel academic recommendation system based on two proven principles: the Principle of Download Persistence and the Principle of Citation Approaching (we prove them based on real-world datasets). The principle of download persistence indicates that classical papers have few decreasing download frequencies since they were published. The principle of citation approaching indicates that a paper which cites a classical paper is likely to cite citations of that classical paper. Our experimental results based on large-scale real-world datasets illustrate Claper can effectively recommend classical papers of high quality to beginners and thus help them enter their research areas. ER - TY - CONF AU - Strohman, Trevor AU - Croft, W. Bruce AU - Jensen, David A2 - T1 - Recommending citations for academic papers T2 - Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval PB - ACM C1 - New York, NY, USA PY - 2007/ CY - VL - IS - SP - 705 EP - 706 UR - http://doi.acm.org/10.1145/1277741.1277868 DO - 10.1145/1277741.1277868 KW - analysis KW - citation KW - item KW - paper KW - recommender KW - research L1 - SN - 978-1-59593-597-7 N1 - N1 - AB - We approach the problem of academic literature search by considering an unpublished manuscript as a query to a search system. We use the text of previous literature as well as the citation graph that connects it to find relevant related material. We evaluate our technique with manual and automatic evaluation methods, and find an order of magnitude improvement in mean average precision as compared to a text similarity baseline. ER - TY - JOUR AU - Loo, Yueh-Lin AU - Willett, Robert L. AU - Baldwin, Kirk W. AU - Rogers, John A. T1 - Additive, nanoscale patterning of metal films with a stamp and a surface chemistry mediated transfer process: Applications in plastic electronics JO - Applied Physics Letters PY - 2002/ VL - 81 IS - 3 SP - 562 EP - 564 UR - http://link.aip.org/link/?APL/81/562/1 DO - 10.1063/1.1493226 KW - display KW - e-ink KW - nano KW - paper L1 - SN - N1 - Additive, nanoscale patterning of metal films with a stamp and a surface chemistry mediated transfer process: Applications in plastic electronics | Issue 3 - Applied Physics Letters N1 - AB - ER -