1. <nav id="jhvnf"></nav>

      <listing id="jhvnf"></listing>

      1. <output id="jhvnf"></output>
            1. <code id="jhvnf"></code>
              1. <var id="jhvnf"><object id="jhvnf"><noscript id="jhvnf"></noscript></object></var>

                1. <nav id="jhvnf"></nav>
                  <listing id="jhvnf"></listing>
                      1. <small id="jhvnf"></small><dd id="jhvnf"><delect id="jhvnf"></delect></dd>
                            1. <span id="jhvnf"></span>

                              自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

                              End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

                              NLPIR SEMINAR Y2019#11


                              In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.


                              This week’s seminar is organized as follows:

                              1. The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
                              2. The lecturer is Zhaoyang Wang , the paper’s title is End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF.
                              3. The seminar will be hosted by Qinghong Jiang.
                              4. Attachment is the paper of this seminar, please download in advance.

                              Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.

                              End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

                              Xuezhe Ma and Eduard Hovy


                              State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of handcrafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no feature engineering or data preprocessing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks — Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain state-of-the-art performance on both datasets—97.55% accuracy for POS tagging and 91.21% F1 for NER.

                              You May Also Like

                              About the Author: nlpvv


                              十一选五投注攻略 捕鱼达人之海底总动员 浙江快乐彩号码 河北11选5交流群 福彩陕西快乐十分钟 彩票大奖排行 图表走势一中彩大数据 彩票方案 36选7好彩一 中国体彩手机网 17094期足彩投注策略 河北快3走试 二肖中特035 欢乐斗地主 半全场最新开奖 广西快乐十分开奖视频