The Pronominal bu-şu and this-that: Rhetorical Structure Theory

In this paper I discuss the rhetorical relations established in two contexts of occurrence of adverbial clauses and appositive relative clauses, as follows: the clauses may be realized either in their canonical form or apart from the main clause, as independent clauses, which I have been calling “floating structures”. My object of investigation isthe equivalence among (a) certain textual-discourse functions performed by such structures, such as “evaluation”, “restatement”, “focus”, among others ─ all pertaining to clause combining at a micro level of text organization; (b) the rhetorical relations considered at a macro level of discourse organization; and (c) the syntactic functions performed by those structures. Basically, this study is carried out within de framework of Rhetorical Structure Theory, according to its development by functionalist researchers from the West coast of North America.Key words: rhetorical relations, textual-discourse functions, clause combining, syntactic functions, functionalism.

Rhetorical relations in university students’ presentations

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In the current study we aim at exploiting discourse structure of Arabic text to automatically finding answers to non-factoid questions ("Why" and "How to"). Our method is based on Rhetorical Structure Theory (RST) that many studies have shown to be a very effective approach for many computational linguistics applications such as (text generation, text summarization and machine translation). For both types of questions we assign one or more rhetorical relations that help discovering the corresponding answers. This is the first Arabic Question Answering system that attempts to answer the "Why" and "How to" questions.

Clarifying characteristics of appropriate argumentative discourse is important for developing computer assisted argumentation systems. We describe the analysis of argumentative discourse structure on the basis of Rhetorical Structure Theory in order to clarify what kind of argumentative discourse structure should be considered appropriate. We think that there exist specific agreement-oriented sequences of rhetorical relations in argumentative discourse that tend to lead to an agreement. We build a small argumentative corpus annotated with rhetorical relations and calculate posteriori probability for rhetorical relations bigrams to investigate what rhetorical relations precede agreement.

Rhetorical Structure Theory (RST) is a theory of text structure that is being extended to serve as a theoretical basis for computational text planning. Text structures in RST are hierarchic, built on small patterns called schemas. The schemas which compose the structural hierarchy of a text describe the functions of the parts rather than their form characteristics. Relations between text parts, comparable to conjunctive relations, are a prominent part of RST’s definitional machinery. Recent work has put RST onto a new definitional basis. This paper details the current status of descriptive RST, along with efforts to create a constructive version for use as a basis for programming a text planner.

Interactional coherence in asynchronous learning networks: A rhetorical approach

The existing discourse parsing systems make use of different theories to put at the basis of processes of building discourse trees. Many of them use Recall, Precision and F-measure to compare discourse tree structures. These measures can be used only on topologically identical structures. However, there are known cases when two different tree structures of the same text can express the same discourse interpretation, or something very similar. In these cases Precision, Recall and F-measures are not so conclusive. In this paper, we propose three new scores for comparing discourse trees. These scores take into consideration more and more constraints. As basic elements of building the discourse structure we use those embraced by two discourse theories: Rhetorical Structure Theory (RST) and Veins Theory, both using binary trees augmented with nuclearity notation. We will ignore the second notation used in RST – the name of relations. The first score takes into account the coverage of inner nodes. The second score complements the first score with the nuclearity of the relation. The third score computes Precisions, Recall and F-measures on the vein expressions of the elementary discourse units. We show that these measures reveal comparable scores there where the differences in structure are not doubled by differences in interpretation.Keywordsdiscourse parserRhetorical Structure TheoryVeins Theoryevaluationdiscourse tree structure

This paper discusses the planning of multisentential text by computer. In order to construct coherent paragraphs, we have been using relations from Rhetorical Structure Theory (RST) operationalized as plans. The paper first describes, in some detail, the current method of planning a paragraph using operationalized RST relation/plans. It then makes two points that illustrate why RST relation/plans are an ideal tool for planning paragraphs. First, these relation/plans can be shown to combine the best features of paragraph-sized schemas and clause-sized planning rules under a top-down planning regime in a way which affords much flexibility to the user. Second, RST relation/plans can support both standard top-down planning and open-ended conversation-like behavior; a small difference in treatment gives rise to either paradigm.

Discourse structures have a central role in several computational tasks, such as question-answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector Machine (SVM) classification. SVM classifiers are trained and applied to discourse segmentation and relation labeling. By combining labeling with a greedy bottom-up tree building approach, we are able to create accurate discourse trees in linear time complexity. Importantly, our parser can parse entire texts, whereas the publicly available parser SPADE (Soricut and Marcu 2003) is limited to sentence level analysis. HILDA outperforms other discourse parsers for tree structure construction and discourse relation labeling. For the discourse parsing task, our system reaches 78.3% of the performance level of human annotators. Compared to a state-of-the-art rule-based discourse parser, our system achieves a performance increase of 11.6%.

이 연구는 전문 필자의 문장이 다른 필자의 문장에 비해 짧다는 점에 착안하여, 연결어미가 문맥에서 가지는 역할과 의미를 보다 구체적으로 분석해 보는 데에 그 목적이 있다.BR 실제 언어생활에서 화자는 ‘문맥’을 고려하여 연결어미나 종결어미를 사용한다. 이에 따라 연결어미의 의미를 ‘담화 · 텍스트’ 차원에서 분석하고자 전문 필자가 쓴 글인 ‘칼럼’을 대상으로, ‘담화 · 텍스트’를 분석하는 도구 중 하나인 ‘수사구조이론(RST)’을 적용하여 ‘연결어미’가 쓰인 문맥의 수사 의미 관계를 분석해 보았다.BR 칼럼에 제시된 ‘-고’는 연결어미 중 가장 많이 쓰였다. ‘-고’는 대등하게 두 사실을 연결하는 의미이나 보다 구체적으로 ‘상세설명(Elaboration)’, ‘의도적 결과(Volitional Result)’, ‘연속(Sequence)’, ‘대조(Contrast)’, ‘배경(Background)’, ‘증거(Evidence)’, ‘연결(joint)’의 7가지 수사 의미 관계로 쓰이며, ‘연속(Sequence)’의 의미 관계로 가장 많이 쓰였다. ‘-지만’은 앞의 내용과 반대되는 내용을 말한다는 의미를 가지므로, ‘대조(Contrast)’의 의미로 쓰일 것으로 예상되지만, 실제로 ‘대비(Antithesis)’와 ‘양보(Concession)’의 문맥에서 주로 쓰였다. ‘-(으)며’는 ‘연결(joint)’의 문맥뿐만 아니라 보다 구체적으로 ‘상세설명(Elaboration)’이나 ‘증거(Evidence)’ 등의 수사 의미 관계를 갖는 문맥에도 쓰였다. 또 ‘연결(joint)’의 문맥에서 ‘-(으)며’와 ‘-고’가 함께 쓰인다는 것을 알 수 있었다.BR 이 연구는 분석 칼럼 수가 적고 타당성 검증이 이루어지지 않았다는 한계를 가지고 있으나, 전문 필자들이 연결어미를 쓰는 문맥과 그러한 문맥이 갖는 의미를 분석했다는 점, 응용 언어학 분야에 도움이 될 수 있다는 점에서 의의가 있을 수 있다.

This paper identifies a major problem in planning discourse, and then points to a solution. The problem is that of the relationship between models for monologue and dialogue. Rhetorical structure theory (RST) is selected as the current best prospect for modelling monologue. A model for exchange structure is then outlined — the systemic flowchart model — and we conclude by showing how the two can be related to each other in an integrated overall model.

Emotion-Cause Pair Extraction (ECPE) aims to identify and extract emotion clauses and their corresponding cause clauses from unannotated emotional texts. Previous approaches primarily leveraged contextual features or attention weights as semantic information to enhance feature representations. However, these methods often overlook the additional causal information that external knowledge can bring to ECPE. Given the evident inferential relationship between emotions and their causes, we propose a novel model called Rhetorical Structure Theory and Inference-Aware Graph networks (RSTIAG) to address the challenges posed by ECPE. This model constructs a semantic dependency space grounded in discourse-level rhetorical structure theory and a symbolic reasoning space based on an inference-aware graph. By integrating the introduced knowledge through a space fusion co-attention mechanism and modeling the dependencies between these two spaces, the model enables clauses to simultaneously capture diverse knowledge from both spaces. Additionally, we enrich the information dimensions of emotion-cause pairs by instantiating the reasoning process from the perspectives of causality and implicit inference. To substantiate the effectiveness of our proposed RSTIAG model, we undertake comprehensive experiments. The results demonstrate that it significantly outperforms 11 baseline models on an English benchmark corpus, confirming its effectiveness.

These definitions and protocols are intended as an online supplement to work in the course of publication. This work focuses on the tagging of corpora of argumentative writing with logical relations, and presents the frameworks used in the text analysis at the base of the tagging.

Rhetorical Structure Theory