Annotation upon Annotation: Adding Signalling Information to a Corpus of Discourse Relations

Taboada, Maïté; Das, Debopam

doi:10.5087/dad.2013.211

Cited by 31 publications

(43 citation statements)

References 64 publications

(83 reference statements)

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“…We have as of yet not been able to find or compile another translation corpus on which we can replicate this study. However, in as far as the data are comparable, the patterns in the marking of coherence relations found in the current study are very similar to patterns reported on the basis of the PDTB (Asr and Demberg, 2012) and the RST Treebank (Taboada and Das, 2013), both of which use newspaper texts from the Wall Street Journal.…”

Section: Parametersupporting

confidence: 90%

Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study

Hoek

Zufferey

Evers-Vermeul

et al. 2017

Journal of Pragmatics

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Coherence relations can be made linguistically explicit by means of connectives (e.g., but, because) or cue phrases (e.g., on the other hand, which is why), but can also be left implicit and conveyed through the juxtaposition of two clauses or sentences. However, it seems that not all relations are equally easy to reconstruct when they are implicit. In this paper, we explore which features of coherence relations make them more, or less, likely to be conveyed implicitly. We adopt the assumption that expected relations are more often implicit than relations that are not expected, and propose to determine a relation's expectedness using the notion of cognitive complexity. We test our hypotheses by means of a parallel corpus study, in which we analyze the translations of explicit English coherence relations from the Europarl Direct corpus into four target languages: Dutch, German, French, and Spanish. We find that cognitive complexity indeed influences the linguistic marking of coherence relations, and that this does not vary between the languages in our corpus. In addition, we find that a relation's relational and syntactic dependency also influences its linguistic marking, but that these measures are not completely independent of relation type.

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Section: Parametersupporting

confidence: 90%

Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study

Hoek

Zufferey

Evers-Vermeul

et al. 2017

Journal of Pragmatics

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“…Also, new features may be explored, such as various types of discourse signals (beyond discourse markers) proposed by [29] and the use of semantic knowledge, such as polarity and synonymy.…”

Section: Discussionmentioning

confidence: 99%

Adaptation of Discourse Parsing Models for the Portuguese Language

Maziero

Hirst

Pardo

2015

2015 Brazilian Conference on Intelligent Systems (BRACIS)

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Abstract-Discourse parsing in Portuguese has two critical limitations. The first is that the task has been explored using only symbolic approaches, i.e., using manually extracted lexical patterns. The second is related to the domain of the lexical patterns, which were extracted through the analysis of a corpus of academic texts, generating many domain-specific patterns. For English, many approaches have been explored using machine learning with features based on a prominent lexicon-syntax notion of dominance sets. In this paper, two works were adapted to Portuguese, improving the results, outperforming the baselines and previous works for Portuguese, considering the task of rhetorical relation identification.

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“…The extracted cue phrases are then used to infer the type of rhetorical relation. For this task we utilize a predefined list of rhetorical cue words adapted from the work of Taboada and Das (2013), which assigns them to the relation that they most likely trigger. For example, the transformation rule in Figure 2 spec-ifies that "although" is the cue word here, which is mapped to a "Contrast" relationship.…”

Section: Transformation Stagementioning

confidence: 99%

Transforming Complex Sentences into a Semantic Hierarchy

Niklaus¹,

Cetto²,

Freitas³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a twolayered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online.

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Annotation upon Annotation: Adding Signalling Information to a Corpus of Discourse Relations

Cited by 31 publications

References 64 publications

Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study

Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study

Adaptation of Discourse Parsing Models for the Portuguese Language

Transforming Complex Sentences into a Semantic Hierarchy

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