DESYR：基于定义和句法表示的Web声明检测
DESYR: Definition and Syntactic Representation Based Claim Detection on the Web
摘要：
主张的形成是论证挖掘的核心。区分声明和非声明对于人类和机器来说都是困难的，因为两者之间存在潜在的语言差异，并且基于定义的形式化不充分。此外，在线社交媒体使用的增加导致网络上以非正式文本形式提供的未经请求的信息激增。为了解决上述问题，在本文中，我们提出了DESYR。这是一个框架，旨在取消非正式的基于Web的文本，通过利用层次表示学习（依赖启发庞加莱嵌入），基于定义的对齐和功能投影的组合所述问题。我们放弃了对计算机繁重的语言模型进行微调，转而采用一种更以领域为中心但更轻便的方法。实验结果表明，DESYR建立在国家的最先进的系统在四个基准索赔数据集，其中大部分是由非正式文本。我们看到在LESA-Twitter数据集上增加了3个claim-F1点，在线评论（OC）数据集上增加了1个claim-F1点和9个macro-F1点，Web话语（WD）数据集上增加了24个claim-F1点和17个macro-F1点，微文本（MT）数据集上增加了8个claim-F1点和5个macro-F1点。我们还对结果进行了广泛的分析。我们制作了一个100-D的庞加莱变体的预训练版本以及源代码。
英文摘要：
The formulation of a claim rests at the core of argument mining. To demarcate between a claim and a non-claim is arduous for both humans and machines, owing to latent linguistic variance between the two and the inadequacy of extensive definition-based formalization. Furthermore, the increase in the usage of online social media has resulted in an explosion of unsolicited information on the web presented as informal text. To account for the aforementioned, in this paper, we proposed DESYR. It is a framework that intends on annulling the said issues for informal web-based text by leveraging a combination of hierarchical representation learning (dependency-inspired Poincare embedding), definition-based alignment, and feature projection. We do away with fine-tuning computer-heavy language models in favor of fabricating a more domain-centric but lighter approach. Experimental results indicate that DESYR builds upon the state-of-the-art system across four benchmark claim datasets, most of which were constructed with informal texts. We see an increase of 3 claim-F1 points on the LESA-Twitter dataset, an increase of 1 claim-F1 point and 9 macro-F1 points on the Online Comments(OC) dataset, an increase of 24 claim-F1 points and 17 macro-F1 points on the Web Discourse(WD) dataset, and an increase of 8 claim-F1 points and 5 macro-F1 points on the Micro Texts(MT) dataset. We also perform an extensive analysis of the results. We make a 100-D pre-trained version of our Poincare-variant along with the source code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
