CRPC-DB – A Discourse Bank for Portuguese

info:eu-repo/semantics/publishedVersio

Annotating the meaning of discourse connectives by looking at their translation: The translation-spotting technique

The various meanings of discourse connectives like while and however are difficult to identify and annotate, even for trained human annotators. This problem is all the more important that connectives are salient textual markers of cohesion and need to be correctly interpreted for many NLP applications. In this paper, we suggest an alternative route to reach a reliable annotation of connectives, by making use of the information provided by their translation in large parallel corpora. This method thus replaces the difficult explicit reasoning involved in traditional sense annotation by an empirical clustering of the senses emerging from the translations. We argue that this method has the advantage of providing more reliable reference data than traditional sense annotation. In addition, its simplicity allows for the rapid constitution of large annotated datasets

Turkish Discourse Bank: Porting a discourse annotation style to a morphologically rich language

This paper briefly describes the Turkish Discourse Bank, the first publicly available annotated discourse resource for Turkish. It focuses on the challenges posed by annotating Turkish, a free word order language with rich inflectional and derivational morphology. It shows the usefulness of the PDTB style annotation but points out the need to expand this annotation style with the needs of the target language

The annotation scheme of the Turkish Discourse Bank and an evaluation of inconsistent annotations

In this paper, we report on the annotation procedures we developed for annotating the Turkish Discourse Bank (TDB), an effort that extends the Penn Discourse Tree Bank (PDTB) annotation style by using it for annotating Turkish discourse. After a brief introduction to the TDB, we describe the annotation cycle and the annotation scheme we developed, defining which parts of the scheme are an extension of the PDTB and which parts are different. We provide inter-coder reliability calculations on the first and second arguments of some connectives and discuss the most important sources of disagreement among annotators

Using the Cognitive Approach to Coherence Relations for Discourse Annotation

The Cognitive approach to Coherence Relations (Sanders, Spooren, & Noordman, 1992) was originally proposed as a set of cognitively plausible primitives to order coherence relations, but is also increasingly used as a discourse annotation scheme. This paper provides an overview of new CCR distinctions that have been proposed over the years, summarizes the most important discussions about the operationalization of the primitives, and introduces a new distinction (disjunction) to the taxonomy to improve the descriptive adequacy of CCR. In addition, it reflects on the use of the CCR as an annotation scheme in practice. The overall aim of the paper is to provide an overview of state-of-the-art CCR for discourse annotation that can form, together with the original 1992 proposal, a comprehensive starting point for anyone interested in annotating discourse using CCR

Corpus-driven Semantics of Concession: Where do Expectations Come from?

                                                                                              Concession is one of the trickiest semantic discourse relations appearing in natural language. Many have tried to sub-categorize Concession and to define formal criteria to both distinguish its subtypes as well as for distinguishing Concession from the (similar) semantic relation of Contrast. But there is still a lack of consensus among the different proposals. In this paper, we focus on those approaches, e.g. (Lagerwerf 1998), (Winter & Rimon 1994), and (Korbayova & Webber 2007), assuming that Concession features two primary interpretations, "direct" and "indirect". We argue that this two way classification falls short of accounting for the full range of variants identified in naturally occurring data. Our investigation of one thousand Concession tokens in the Penn Discourse Treebank (PDTB) reveals that the interpretation of concessive relations varies according to the source of expectation. Four sources of expectation are identified. Each is characterized by a different relation holding between the eventuality that raises the expectation and the eventuality describing the expectation. We report a) a reliable inter-annotator agreement on the four types of sources identified in the PDTB data, b) a significant improvement on the annotation of previous disagreements on Concession-Contrast in the PDTB and c) a novel logical account of Concession using basic constructs from Hobbs' (1998) logic. Our proposal offers a uniform framework for the interpretation of Concession while accounting for the different sources of expectation by modifying a single predicate in the proposed formulae

Examples and Specifications that Prove a Point: Identifying Elaborative and Argumentative Discourse Relations

Examples and specifications occur frequently in text, but not much is known about how they function in discourse and how readers interpret them. Looking at how they’re annotated in existing discourse corpora, we find that annotators often disagree on these types of relations; specifically, there is disagreement about whether these relations are elaborative (additive) or argumentative (pragmatic causal). To investigate how readers interpret examples and specifications, we conducted a crowdsourced discourse annotation study. The results show that these relations can indeed have two functions: they can be used to both illustrate/specify a situation and serve as an argument for a claim. These findings suggest that examples and specifications can have multiple simultaneous readings. We discuss the implications of these results for discourse annotation.&nbsp