@inProceedings{
 title = {Automatic Entity Relation Extraction Based on Maximum Entropy},
 type = {inProceedings},
 year = {2006},
 identifiers = {[object Object]},
 pages = {540-544},
 volume = {1},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021496},
 month = {10},
 publisher = {IEEE},
 id = {c8ef6976-d38f-3a0f-95f0-91591760d0ce},
 created = {2012-04-01T16:32:49.000Z},
 accessed = {2012-03-22},
 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 = {named entity recognition},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zhang2006c},
 private_publication = {false},
 abstract = {Entity relation extraction (RE) is an very important research domain in information extraction, we can regard RE as a classification problem in this paper, RE is still original study field in Chinese language now, maximum entropy (ME)-based machine learning is the first time to be used to extract entity relations between named entities from Chinese texts, Thirteen features have been designed for entity relation extraction, which includes morphology, grammar and semantic feature. The system architecture for RE has been constructed. Experiment shows that the performance is promising. So it is useful for ME-based machine learning to solve RE problem},
 bibtype = {inProceedings},
 author = {Zhang, Suxiang and Wen, Juan and Wang, Xiaojie and Li, Lei},
 booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}
}

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