@article{
 title = {Extracting relation information from text documents by exploring various types of knowledge},
 type = {article},
 year = {2007},
 identifiers = {[object Object]},
 keywords = {feature based relation extraction,information extraction,knowledge exploration,support vector machines},
 pages = {969–982},
 volume = {43},
 websites = {http://linkinghub.elsevier.com/retrieve/pii/S0306457306001543},
 publisher = {Elsevier},
 id = {a7537977-155c-381e-a4c4-d6122e71f525},
 created = {2011-01-22T19:51:02.000Z},
 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},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zhou2007a},
 private_publication = {false},
 abstract = {Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using support vector machines. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while current commonly used features from full parsing give limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. This indicates that a cheap and robust solution in relation extraction can be achieved without decreasing too much in performance. We also demonstrate how semantic information such as WordNet, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE benchmark corpora shows that effective incorporation of diverse features enables our system outperform previously best-reported systems. It also shows that our feature-based system significantly outperforms tree kernel-based systems. This suggests that current tree kernels fail to effectively explore structured syntactic information in relation extraction.},
 bibtype = {article},
 author = {Zhou, G and Zhang, M},
 journal = {Information Processing & Management},
 number = {4}
}

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