Neural-based RST Parsing And Analysis In Persuasive Discourse
Most of the existing studies of language use in social media content have focused on the surface-level linguistic features (e.g., function words and punctuation marks) and the semantic level aspects (e.g., the topics, sentiment, and emotions) of the comments. The writer’s strategies of constructing and connecting text segments have not been widely explored even though this knowledge is expected to shed light on how people reason in online environments. Contributing to this analysis direction for social media studies, we build an openly accessible neural RST parsing system that analyzes discourse relations in an online comment. Our experiments demonstrate that this system achieves comparable performance among all the neural RST parsing systems. To demonstrate the use of this tool in social media analysis, we apply it to identify the discourse relations in persuasive and non-persuasive comments and examine the relationships among the binary discourse tree depth, discourse relations, and the perceived persuasiveness of online comments. Our work demonstrates the potential of analyzing discourse structures of online comments with our system and the implications of these structures for understanding online communications.
Code (0)
등록된 구현이 없습니다.
Tasks
PersuasivenessSimilar Papers 제목 키워드 기반
On the role of discourse relations in persuasive texts
This paper investigates the use of explicitly signalled discourse relations in persuasive texts. We present a corpus study where we control for speaker and topic and show that the distribution of different discourse conn…
Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing
One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so …
Dependency ParsingDiscourse ParsingWhat Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on …
PersuasivenessA Conditional Splitting Framework for Efficient Constituency Parsing
We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. Our parsing model estimates the conditional pr…
Constituency ParsingDiscourse ParsingDiscourse SegmentationSegmentationTowards Domain-Independent Supervised Discourse Parsing Through Gradient Boosting
Discourse analysis and discourse parsing have shown great impact on many important problems in the field of Natural Language Processing (NLP). Given the direct impact of discourse annotations on model performance and int…
Discourse ParsingDomain Adaptation