@inproceedings{f049b1e125fd4aa59fd7153cd46cbc93,
title = "LitWay, Discriminative Extraction for Different Bio-Events",
abstract = "Even a simple biological phenomenon may introduce a complex network of molecular interactions. Scientific literature is one of the trustful resources delivering knowledge of these networks. We propose LitWay, a system for extracting semantic relations from texts. Lit- Way utilizes a hybrid method that combines both a rule-based method and a machine learning-based method. It is tested on the SeeDev task of BioNLP-ST 2016, achieves the state-of-the-art performance with the F-score of 43.2\%, ranking first of all participating teams. To further reveal the linguistic characteristics of each event, we test the system solely with syntactic rules or machine learning, and different combinations of two methods. We find that it is difficult for one method to achieve good performance for all semantic relation types due to the complication of bio-events in the literatures.",
author = "Chen Li and Zhiqiang Rao and Xiangrong Zhang",
note = "Publisher Copyright: {\textcopyright}2016 Association for Computational Linguistics.; ACL 2016 4th BioNLP Shared Task Workshop 2016 ; Conference date: 13-08-2016 Through 13-08-2016",
year = "2016",
language = "英语",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "32--41",
editor = "Claire Nedellec and Robert Bossy and Jin-Dong Kim",
booktitle = "ACL 2016 - Proceedings of the 4th BioNLP Shared Task Workshop",
}