Text-level Discourse Dependency Parsing

Li, Sujian; Wang, Liang; Cao, Zhigang; Li, Wenjie

doi:10.3115/v1/p14-1003

Cited by 88 publications

(142 citation statements)

References 21 publications

(30 reference statements)

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“…For relations between non-EDU higher spans, the recursive head was used. It is unclear how Li et al (2014) deal with binary multinucleus relations like CONTRAST for example; it is not clear how to calculate the recursive head of the span. 1 In such cases an arbitrary decisionlike always taking as the nucleus the leftmost or the rightmost span-has to be taken.…”

Section: Model 41 Dependency Structuresmentioning

confidence: 99%

“…the one firstly introduced in the text. Hirao et al (2013) and Li et al (2014) later followed a similar strategy for the creation of dependency structures for RST. Every single nucleussatellite relation was transformed into a dependency relation with the governor being the EDU representing the nucleus and the dependent being the satellite.…”

Section: Model 41 Dependency Structuresmentioning

confidence: 99%

“…and "how far can tree decoding algorithms get us in multi-party dialogue? ", our first decoder is the classic Maximum Spanning Trees (MST) algorithm-used by McDonald et al (2005) for syntactic dependency parsing as well as by Muller et al (2012) and Li et al (2014) for discourse parsing-tweaking it in order to produce structures that are closer to the ones specific to multiparty dialogue. We are looking for:…”

Section: Decodersmentioning

confidence: 99%

“…Moreover, the fact that our data come from chats renders the use of syntactic and lexical information useless since people take great liberties in expressing themselves lexically and syntactically. We use the dependency parsing paradigm as has been done in the past (Muller et al, 2012;Li et al, 2014). We learn local probability distributions and then use MST for decoding.…”

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confidence: 99%

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Discourse parsing for multi-party chat dialogues

Afantenos¹,

Kow²,

Asher³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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In this paper we present the first ever, to the best of our knowledge, discourse parser for multi-party chat dialogues. Discourse in multi-party dialogues dramatically differs from monologues since threaded conversations are commonplace rendering prediction of the discourse structure compelling. Moreover, the fact that our data come from chats renders the use of syntactic and lexical information useless since people take great liberties in expressing themselves lexically and syntactically. We use the dependency parsing paradigm as has been done in the past (Muller et al., 2012;Li et al., 2014). We learn local probability distributions and then use MST for decoding. We achieve 0.680 F 1 on unlabelled structures and 0.516 F 1 on fully labeled structures which is better than many state of the art systems for monologues, despite the inherent difficulties that multi-party chat dialogues have.

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Section: Model 41 Dependency Structuresmentioning

confidence: 99%

Section: Model 41 Dependency Structuresmentioning

confidence: 99%

Section: Decodersmentioning

confidence: 99%

mentioning

confidence: 99%

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Discourse parsing for multi-party chat dialogues

Afantenos¹,

Kow²,

Asher³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…The necessary conditions are also in place for such a task. The release of the RST-DT and PDTB has attracted a significant amount of research on discourse parsing Duverle and Prendinger, 2009; Subba and Di Eugenio, 2009;Zhou et al, 2010; Feng and Hirst, 2012; Park and Cardie, 2012; Wang et al, 2012;Biran and McKeown, 2013; Lan et al, 2013; Feng and Hirst, 2014; Ji and Eisenstein, 2014; Li and Nenkova, 2014;Li et al, 2014;Lin et al, 2014;, and the momentum is building. Almost all of these recent attempts at discourse parsing use machine learning techniques, which is consistent with the theme of the CoNLL conference.…”

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confidence: 99%

Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

2015

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The CoNLL-2015 Shared Task is on Shallow Discourse Parsing, a task focusing on identifying individual discourse relations that are present in a natural language text. A discourse relation can be expressed explicitly or implicitly, and takes two arguments realized as sentences, clauses, or in some rare cases, phrases. Sixteen teams from three continents participated in this task. For the first time in the history of the CoNLL shared tasks, participating teams, instead of running their systems on the test set and submitting the output, were asked to deploy their systems on a remote virtual machine and use a web-based evaluation platform to run their systems on the test set. This meant they were unable to actually see the data set, thus preserving its integrity and ensuring its replicability. In this paper, we present the task definition, the training and test sets, and the evaluation protocol and metric used during this shared task. We also summarize the different approaches adopted by the participating teams, and present the evaluation results. The evaluation data sets and the scorer will serve as a benchmark for future research on shallow discourse parsing. IntroductionThe shared task for the Nineteenth Conference on Computational Natural Language Learning (CoNLL-2015) is on Shallow Discourse Parsing (SDP). In the course of the sixteen CoNLL shared tasks organized over the past two decades, progressing gradually to tackle phenomena at the word and phrase level phenomena and then the sentence and extra-sentential level, it was only very recently that discourse level processing has been addressed, with coreference resolution (Pradhan et al., 2011; Pradhan et al., 2012). The 2015 shared task takes the community a step further in that direction, with the potential to impact scores of richer language applications (Webber et al., 2012).Given an English newswire text as input, the goal of the shared task is to detect and categorize discourse relations between discourse segments in the text. Just as there are different grammatical formalisms and representation frameworks in syntactic parsing, there are also different conceptions of the discourse structure of a text, and data sets annotated following these different theoretical frameworks (Stede, 2012; Webber et al., 2012; Prasad and Bunt, 2015). For example, the RST-DT Corpus (Carlson et al., 2003) is based on the Rhetorical Structure Theory of Mann and Thompson (1988) and produces a complete treestructured RST analysis of a text, whereas the Penn Discourse TreeBank (PDTB) Prasad et al., 2014) provides a shallow representation of discourse structure, in that each discourse relation is annotated independently of other discourse relations, leaving room for a high-level analysis that may attempt to connect them. For the CoNLL-2015 shared task, we chose to use the PDTB, as it is currently the largest data set annotated with discourse relations. The necessary conditions are also in place for such a task. The release of the RST-DT and PDTB has attracted a signi...

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Discourse-Driven Argument Mining in Scientific Abstracts

Accuosto

Saggion

2019

Natural Language Processing and Information Systems

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Argument mining consists in the automatic identification of argumentative structures in texts. In this work we address the open question of whether discourse-level annotations can contribute to facilitate the identification of argumentative components and relations in scientific literature. We conduct a pilot study by enriching a corpus of computational linguistics abstracts that contains discourse annotations with a new argumentative annotation level. The results obtained from preliminary experiments confirm the potential value of the proposed approach.

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Text-level Discourse Dependency Parsing

Cited by 88 publications

References 21 publications

Discourse parsing for multi-party chat dialogues

Discourse parsing for multi-party chat dialogues

Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

Discourse-Driven Argument Mining in Scientific Abstracts

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