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

INTRO

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.

Arrangement

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

Abstract

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.

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