The GUM corpus: creating multilayer resources in the classroom

Zeldes, Amir

doi:10.1007/s10579-016-9343-x

Cited by 178 publications

(159 citation statements)

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“…First, given that we can now create large datasets, we intend to experiment on structure prediction with neural discourse parsers, which so far have delivered rather disappointing results. Second, an obvious next step is working on the integration of nuclearity and relation prediction to create complete RST annotations for documents from auxiliary tasks and to extend our evaluations (Zeldes, 2017). Third, we will study synergies between discourse parsing and further auxiliary tasks, eventually creating a single, joint system to generate globally high-quality discourse trees.…”

Section: Discussionmentioning

confidence: 99%

Predicting Discourse Structure using Distant Supervision from Sentiment

Huber

Carenini

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Discourse parsing could not yet take full advantage of the neural NLP revolution, mostly due to the lack of annotated datasets. We propose a novel approach that uses distant supervision on an auxiliary task (sentiment classification), to generate abundant data for RSTstyle discourse structure prediction. Our approach combines a neural variant of multipleinstance learning, using document-level supervision, with an optimal CKY-style tree generation algorithm. In a series of experiments, we train a discourse parser (for only structure prediction) on our automatically generated dataset and compare it with parsers trained on human-annotated corpora (news domain RST-DT and Instructional domain). Results indicate that while our parser does not yet match the performance of a parser trained and tested on the same dataset (intra-domain), it does perform remarkably well on the much more difficult and arguably more useful task of inter-domain discourse structure prediction, where the parser is trained on one domain and tested/applied on another one.

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Section: Discussionmentioning

confidence: 99%

Predicting Discourse Structure using Distant Supervision from Sentiment

Huber

Carenini

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…Moreover, there are certain linguistic devices that systematically signal certain discourse relations: some are generic signals across the board while others are indicative of particular relations in certain contexts. Consider the following example from the Georgetown University Multilayer (GUM) corpus (Zeldes, 2017), 1 in which the two textual units connected by the DM but form a CONTRAST relation, meaning that the contents of the two textual units are comparable yet not identical.…”

Section: Introductionmentioning

confidence: 99%

Beyond The Wall Street Journal: Anchoring and Comparing Discourse Signals across Genres

Liu

2019

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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Recent research on discourse relations has found that they are cued not only by discourse markers (DMs) but also by other textual signals and that signaling information is indicative of genres. While several corpora exist with discourse relation signaling information such as the Penn Discourse Treebank (PDTB, Prasad et al. 2008) and the Rhetorical Structure Theory Signalling Corpus (RST-SC, Das and Taboada 2018), they both annotate the Wall Street Journal (WSJ) section of the Penn Treebank (PTB, Marcus et al. 1993), which is limited to the news domain. Thus, this paper adapts the signal identification and anchoring scheme to three more genres, examines the distribution of signaling devices across relations and genres, and provides a taxonomy of indicative signals found in this dataset.

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“…Taking English language as an example, we adopt the Georgetown University Multilayer Corpus (GUM) (Zeldes, 2017) in UD 2.2 and SUD 2.2 projects. Both versions of the treebank are consisted of seven genres, viz.…”

Section: Methodsmentioning

confidence: 99%

Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)

2019

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The use of corpora for NLP and linguistics has only increased in recent years. In NLP, machine learning systems are by nature data-intensive, and in linguistics there is a renewed interest in the empirical validation of linguistic theory, particularly through corpus evidence. While the first statistical parsers have long been trained on the Penn treebank phrase structures, dependency treebanks, whether natively annotated with dependencies, or converted from phrase structures, have become more and more popular, as evidenced by the success of the Universal Dependency project, currently uniting 120 treebanks in 80 languages, annotated in the same dependency-based scheme. The availability of these resources has boosted empirical quantitative studies in syntax. It has also lead to a growing interest in theoretical questions around syntactic dependency, its history, its foundations, and the analyses of various constructions in dependency-based frameworks. Furthermore, the availability of large, multilingual annotated data sets, such as those provided by the Universal Dependencies project, has made cross-linguistic analysis possible to an extent that could only be dreamt of only a few years ago. In this context it was natural to bring together TLT (Treebanks and Linguistic Theories), the historical conference on treebanks as linguistic resources, Depling (The international conference on Dependency Linguistics), the conference uniting research on models and theories around dependency representations, and UDW (Universal Dependency Workshop), the annual meeting of the UD project itself. Moreover, in order to create a point of contact with the large community working in quantitative linguistics it seemed expedient to create a workshop dedicated to quantitative syntactic measures on treebanks and raw corpora, which gave rise to Quasy, the first workshop on Quantitative Syntax. And this led us to the first SyntaxFest. Because the potential audience and submissions to the four events were likely to have substantial overlap, we decided to have a single reviewing process for the whole SyntaxFest. Authors could choose to submit their paper to one or several of the four events, and in case of acceptance, the program co-chairs would decide which event to assign the accepted paper to.

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The GUM corpus: creating multilayer resources in the classroom

Cited by 178 publications

References 20 publications

Predicting Discourse Structure using Distant Supervision from Sentiment

Predicting Discourse Structure using Distant Supervision from Sentiment

Beyond The Wall Street Journal: Anchoring and Comparing Discourse Signals across Genres

Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)

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