分类:The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
Waleed Ammar, Matthew E. Peters, Chandra Bhagavatula, R. Power, The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
Abstract
This paper describes our submission for the ScienceIE shared task (SemEval2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3).
总结和评论
这篇文章基于对经典的end-to-end 实体关系联合抽取模型（见论文End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures）的改进，实现了一些科学论文中的概念和关系提取算法。
原始模型采用了端到端的神经网络结构来进行建模，通过在双向序列LSTM-RNNs上叠加双向树型结构LSTM-RNNs来捕获单词序列和依赖树的子结构信息。本文在以下几个方面进行了改进：通过神经语言模型进行半监督学习；字符级编码；利用从现有知识库中提取的索引词典；模型集成。
- 任务：从科研论文中识别实体类型（Task / Material / Process）以及抽取实体关系（ Hyponym-of / Synonym-of）
- 模型描述：
- Entity Model
概念地图
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