We propose a new approach for extracting argument structure from natural
language texts that contain an underlying argument. Our approach comprises of
two phases: Score Assignment and Structure Prediction. The Score Assignment
phase trains models to classify relations between argument units (Support,
Attack or Neutral). To that end, different training strategies have been
explored. We identify different linguistic and lexical features for training
the classifiers. Through ablation study, we observe that our novel use of
word-embedding features is most effective for this task. The Structure
Prediction phase makes use of the scores from the Score Assignment phase to
arrive at the optimal structure. We perform experiments on three argumentation
datasets, namely, AraucariaDB, Debatepedia and Wikipedia. We also propose two
baselines and observe that the proposed approach outperforms baseline systems
for the final task of Structure Prediction.

提出了一种从自然语言文本中提取论点结构的新方法，其包括评分分配和结构预测两个阶段，评分分配阶段使用不同的训练策略和词嵌入特征对各种关系进行分类，最终预测出最佳结构，实验表明该方法表现优于基线系统。