Citing for high impact

Shi, Xiaolin; Leskovec, Jure; McFarland, Daniel A.

doi:10.1145/1816123.1816131

Cited by 56 publications

(41 citation statements)

References 24 publications

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“…Rather, they are highly inter-connected in giant citation networks, in which papers cite or are cited by other papers. In a citation network, information flows from one paper to another via the citation relation (Shi et al, 2010). This information flow and the influence of one paper on another are specifically captured by means of citation contexts, i.e., short text segments surrounding a citation's mention.…”

Section: Introductionmentioning

confidence: 99%

Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach

Caragea

Bulgarov

Godea

et al. 2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Given the large amounts of online textual documents available these days, e.g., news articles, weblogs, and scientific papers, effective methods for extracting keyphrases, which provide a high-level topic description of a document, are greatly needed. In this paper, we propose a supervised model for keyphrase extraction from research papers, which are embedded in citation networks. To this end, we design novel features based on citation network information and use them in conjunction with traditional features for keyphrase extraction to obtain remarkable improvements in performance over strong baselines.

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Section: Introductionmentioning

confidence: 99%

Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach

Caragea

Bulgarov

Godea

et al. 2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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show abstract

“…For instance, Shi, Leskovec, and McFarland [18] developed citation projection graphs by investigating citations among publications that a given paper cites. In this study, the authors investigated high-impact and low-impact citation behavior, where "citation impact" is defined as the number of citations a publication receives normalized by the average number of citations of all other publications published in the same year and same area.…”

Section: Citation Recommendation and Contextmentioning

confidence: 99%

Full-text based context-rich heterogeneous network mining approach for citation recommendation

Liu¹,

Yu²,

Guo³

et al. 2014

IEEE/ACM Joint Conference on Digital Libraries

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Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance.

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“…An key role of citation analysis is to measure importance of venues using citation based metrics [25,26]. The impact factor (IF) [8] provided by ISI might be, if not the most satisfactory one, the most popular one for measuring journals.…”

Section: Venue Rankmentioning

confidence: 99%

Searching online book documents and analyzing book citations

Das

et al. 2013

Proceedings of the 2013 ACM Symposium on Document Engineering

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Academic search engines and digital libraries provide convenient online search and access facilities for scientific publications. However, most existing systems do not include books in their collections although several books are freely available online. Academic books are different from papers in terms of their length, contents and structure. We argue that accounting for academic books is important in understanding and assessing scientific impact. We introduce an open-book search engine that extracts and indexes metadata, contents, and bibliography from online PDF book documents. To the best of our knowledge, no previous work gives a systematical study on building a search engine for books.We propose a hybrid approach for extracting title and authors from a book that combines results from CiteSeer, a rule based extractor, and a SVM based extractor, leveraging web knowledge. For "table of contents" recognition, we propose rules based on multiple regularities based on numbering and ordering. In addition, we study bibliography extraction and citation parsing for a large dataset of books. Finally, we use the multiple fields available in books to rank books in response to search queries. Our system can effectively extract metadata and contents from large collections of online books and provides efficient book search and retrieval facilities.

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Citing for high impact

Cited by 56 publications

References 24 publications

Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach

Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach

Full-text based context-rich heterogeneous network mining approach for citation recommendation

Searching online book documents and analyzing book citations

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