Summary of the paper
| Title | Exploiting the Role of Position Feature in Chinese Relation Extraction |
|---|---|
| Authors | Peng Zhang, Wenjie Li, Furu Wei, Qin Lu and Yuexian Hou |
| Abstract | Relation extraction is the task of finding pre-defined semantic relations between two entities or entity mentions from text. Many methods, such as feature-based and kernel-based methods, have been proposed in the literature. Among them, feature-based methods draw much attention from researchers. However, to the best of our knowledge, existing feature-based methods did not explicitly incorporate the position feature and no in-depth analysis was conducted in this regard. In this paper, we define and exploit nine types of position information between two named entity mentions and then use it along with other features in a multi-class classification framework for Chinese relation extraction. Experiments on the ACE 2005 data set show that the position feature is more effective than the other recognized features like entity type/subtype and character-based N-gram context. Most important, it can be easily captured and does not require as much effort as applying deep natural language processing. |
| Language | Single language |
| Topics | Information Extraction, Information Retrieval, Text mining, Knowledge representation |
Full paper |
Exploiting the Role of Position Feature in Chinese Relation Extraction |
Slides |
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| Bibtex | @InProceedings{ZHANG08.540, author = {Peng Zhang, Wenjie Li, Furu Wei, Qin Lu and Yuexian Hou}, title = {Exploiting the Role of Position Feature in Chinese Relation Extraction}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |