@article{
 title = {Keyphrase Extraction in Scientific Publications},
 type = {article},
 year = {2007},
 identifiers = {[object Object]},
 pages = {317-326},
 volume = {4822},
 websites = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.95.9192},
 publisher = {Springer},
 series = {Lecture Notes in Computer Science},
 editors = {[object Object],[object Object],[object Object],[object Object]},
 id = {508ef9a5-fd97-3ccb-97e3-e7dd2b1b4390},
 created = {2011-02-27T18:33:21.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {keyphrase extraction},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Nguyen2007b},
 private_publication = {false},
 abstract = {We present a keyphrase extraction algorithm for scientific publications. Different from previous work, we introduce features that capture the positions of phrases in document with respect to logical sections found in scientific discourse. We also introduce features that capture salient morphological phenomena found in scientific keyphrases, such as whether a candidate keyphrase is an acronyms or uses specific terminologically productive suffixes. We have implemented these features on top of a baseline feature set used by Kea 1. In our evaluation using a corpus of 120 scientific publications multiply annotated for keyphrases, our system significantly outperformed Kea at the p<.05 level. As we know of no other existing multiply annotated keyphrase document collections, we have also made our evaluation corpus publicly available. We hope that this contribution will spur future comparative research.},
 bibtype = {article},
 author = {Nguyen, Thuy Dung and Kan, Min-Yen},
 journal = {Work}
}

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