Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus

Roesiger, Ina

doi:10.18653/v1/w18-0703

Cited by 15 publications

(37 citation statements)

References 18 publications

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“…One group from the University of Stuttgart participated in this subtask (Roesiger, 2018). We summarize here the results; for further detail, see the paper.…”

Section: Taskmentioning

confidence: 99%

“…These results appear broadly comparable to those obtained by Hou et al (2013) over the ISNotes corpus as far as the RST and TRAINS domain are concerned, but much lower for the PEAR domainalthough given the small number of bridging references in this domain ( 354) not too much should be read into this. See Roesiger (2018) for some interesting hypotheses regarding the differences between the two corpora.…”

Section: Taskmentioning

confidence: 99%

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Anaphora Resolution with the ARRAU Corpus

Poesio¹,

Grishina²,

Kolhatkar³

et al. 2018

Proceedings of the First Workshop on Computational Models Of Reference, Anaphora and Coreference

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The ARRAU corpus is an anaphorically annotated corpus of English providing rich linguistic information about anaphora resolution. The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference). Other distinctive features include treating all NPs as markables, including nonreferring NPs; and the annotation of a variety of morphosyntactic and semantic mention and entity attributes, including the genericity status of the entities referred to by markables. The corpus however has not been extensively used for anaphora resolution research so far. In this paper, we discuss three datasets extracted from the ARRAU corpus to support the three subtasks of the CRAC 2018 Shared Taskidentity anaphora resolution over ARRAU-style markables, bridging references resolution, and discourse deixis; the evaluation scripts assessing system performance on those datasets; and preliminary results on these three tasks that may serve as baseline for subsequent research in these phenomena.

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“…One group from the University of Stuttgart participated in this subtask (Roesiger, 2018). We summarize here the results; for further detail, see the paper.…”

Section: Taskmentioning

confidence: 99%

Section: Taskmentioning

confidence: 99%

Anaphora Resolution with the ARRAU Corpus

Poesio¹,

Grishina²,

Kolhatkar³

et al. 2018

Proceedings of the First Workshop on Computational Models Of Reference, Anaphora and Coreference

Self Cite

View full text Add to dashboard Cite

show abstract

“…One aspect of anaphoric interpretation for which there were no previous results with ARRAU is bridging reference. One group from the University of Stuttgart participated in this subtask (Roesiger 2018). We summarize here the results; for further detail, see the paper.…”

Section: Results On Non-referringmentioning

confidence: 99%

“…These results appear broadly comparable to those obtained by Hou et al [121] over the ISNotes corpus as far as the RST and TRAINS domain are concerned, but much lower for the PEAR domain-although given the small number of bridging references in this domain (354) not too much should be read into this. See Roesiger [105] for some interesting hypotheses regarding the differences between the two corpora. Gold…”

Section: The Crac 2018 Shared Taskmentioning

confidence: 99%

“…Their scheme also expands on the definition of mediated from Nissim et al by also including other anaphora among the bridging references, as well as the funct relation. An interesting analysis of the differences between the notion of bridging reference annotated in ARRAU and that annotated in HSNOTE can be found in Roesiger (2018).…”

Section: Other Corpora Annotated For More Complex Forms Of Anaphoric mentioning

confidence: 99%

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Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus

Uryupina

Artstein

Bristot³

et al. 2019

Nat. Lang. Eng.

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This paper presents the second release of arrau, a multigenre corpus of anaphoric information created over 10 years to provide data for the next generation of coreference/anaphora resolution systems combining different types of linguistic and world knowledge with advanced discourse modeling supporting rich linguistic annotations. The distinguishing features of arrau include the following: treating all NPs as markables, including non-referring NPs, and annotating their (non-) referentiality status; distinguishing between several categories of non-referentiality and annotating non-anaphoric mentions; thorough annotation of markable boundaries (minimal/maximal spans, discontinuous markables); annotating a variety of mention attributes, ranging from morphosyntactic parameters to semantic category; annotating the genericity status of mentions; annotating a wide range of anaphoric relations, including bridging relations and discourse deixis; and, finally, annotating anaphoric ambiguity. The current version of the dataset contains 350K tokens and is publicly available from LDC. In this paper, we discuss in detail all the distinguishing features of the corpus, so far only partially presented in a number of conference and workshop papers, and we also discuss the development between the first release of arrau in 2008 and this second one.

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Text-based NP Enrichment

Elazar

Basmova

Goldberg

et al. 2022

Transactions of the Association for Computational Linguistics

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Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding. However, only a fraction of such relations is covered by standard NLP tasks and benchmarks nowadays. In this work, we propose a novel task termed text-based NP enrichment (TNE), in which we aim to enrich each NP in a text with all the preposition-mediated relations—either explicit or implicit—that hold between it and other NPs in the text. The relations are represented as triplets, each denoted by two NPs related via a preposition. Humans recover such relations seamlessly, while current state-of-the-art models struggle with them due to the implicit nature of the problem. We build the first large-scale dataset for the problem, provide the formal framing and scope of annotation, analyze the data, and report the results of fine-tuned language models on the task, demonstrating the challenge it poses to current technology. A webpage with a data-exploration UI, a demo, and links to the code, models, and leaderboard, to foster further research into this challenging problem can be found at: yanaiela.github.io/TNE/.

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Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus

Cited by 15 publications

References 18 publications

Anaphora Resolution with the ARRAU Corpus

Anaphora Resolution with the ARRAU Corpus

Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus

Text-based NP Enrichment

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