Multi-source annotation projection of coreference chains: assessing
            strategies and testing opportunities

Grishina, Yulia; Stede, Manfred

doi:10.18653/v1/w17-1506

Cited by 11 publications

(27 citation statements)

References 12 publications

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“…In both languages, coreference information is obviously best preserved for central pronouns (ex-cept for basic reflexives). It agrees with findings by Grishina and Stede (2017), where they observed higher precision for pronouns than for nominal groups. They suggest that inferior performance for nominal groups may be a result of errors in mention matching.…”

Section: Experiments and Resultssupporting

confidence: 92%

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A Fine-grained Large-scale Analysis of Coreference Projection

Novák¹

2018

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

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We perform a fine-grained large-scale analysis of coreference projection. By projecting gold coreference from Czech to English and vice versa on Prague Czech-English Dependency Treebank 2.0 Coref, we set an upper bound of a proposed projection approach for these two languages. We undertake a detailed thorough analysis that combines the analysis of projection's subtasks with analysis of performance on individual mention types. The findings are accompanied with examples from the corpus.

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Section: Experiments and Resultssupporting

confidence: 92%

“…by Postolache et al (2006) and Grishina and Stede (2017)). There are several factors affecting the score values, in which these experiments certainly differ: a target language, a range of expressions annotated with coreference, quality of alignment, evaluation measure, etc.…”

Section: Experiments and Resultsmentioning

confidence: 88%

A Fine-grained Large-scale Analysis of Coreference Projection

Novák¹

2018

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

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“…6 Furthermore, the choice of the source language(s) in respect to the target language is also an interesting factor that influences the projection re-5 Probably due to erroneous annotations on the source side, as the authors themselves acknowledge. 6 However, combining coreference annotations coming from several sources is not a trivial task, as shown in Grishina and Stede (2017). sults; however, this issue needs to be investigated further by comparing the quality of a projection approach for different languages in the same setting.…”

Section: Discussionmentioning

confidence: 99%

Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

Ogrodniczuk¹,

Ng²

2017

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ii IntroductionMany NLP researchers, especially those not working in the area of discourse processing, tend to equate coreference resolution with the sort of coreference that people did in MUC, ACE, and OntoNotes, having the impression that coreference is a well-worn task owing in part to the large number of papers reporting results on the MUC/ACE/OntoNotes corpora. This is an unfortunate misconception: the previous SemEval 2010 and CoNLL 2012 shared tasks on coreference resolution have largely focused on entity coreference, which constitutes only one of the many kinds of coreference relations that were discussed in theoretical and computational linguistics in the past few decades. In fact, by focusing on entity coreference resolution, NLP researchers have only scratched the surface of the wealth of interesting problems in coreference resolution.The decision to focus on entity coreference resolution was initially made by information extraction (IE) researchers when coreference was selected as one of the tasks in the MUC-6 coreference in 1995. Many interesting kinds of coreference relations, such as bridging and reference to abstract entities, were left out not because they were not important, but because "it was felt that the menu was simply too ambitious". It turned out that this decision had an important consequence: the progress made in coreference research in the past two decades was largely driven by the availability of coreferenceannotated corpora such as MUC, ACE, and OntoNotes, where entity coreference was the focus.Being aware of other fora gathering coreference-related papers (such as LAW, DiscoMT or EVENTS), in 2016 we started a new workshop on the single topic of knowledge-oriented coreference resolution under the name of Coreference Resolution Beyond OntoNotes (CORBON) that would bring together researchers who were interested in under-investigated coreference phenomena, willing to contribute both theoretical and applied computational work on coreference resolution, especially for languages other than English, less-researched forms of coreference and new applications of coreference resolution.The success of the first edition of the workshop (held in conjunction with NAACL HLT 2016 in San Diego, USA) and our intention to verify the role of the Europe-based researchers in the field encouraged us to organise the second edition of the workshop in conjunction with EACL 2017 in Valencia, Spain. Our call attracted 12 submissions (nine from European research institutions and three from India). We are pleased to see that the submissions covered not only a variety of less-studied languages in the coreference community (e.g., Basque, French, German, Polish, Portuguese, Russian, and Tamil) but also many under-investigated topics in coreference resolution (e.g., feature representation, the use of semantics and deep syntax for coreference resolution, difficult cases of anaphora, and the use of coreference chains in high-level natural language applications). Each submission was rigorously reviewed by three to five ...

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“…While crosslingual projection has demonstrated considerable success in various NLP applications like POS tagging and syntactic parsing, it has been less successful in coreference resolution, performing with 30% less precision than monolingual variants (Grishina and Stede 2017).…”

Section: Projection-based Approachmentioning

confidence: 99%

Different German and English Coreference Resolution Models for Multi-domain Content Curation Scenarios

Srivastava

Weber

Bourgonje

et al. 2018

Lecture Notes in Computer Science

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Abstract. Coreference Resolution is the process of identifying all words and phrases in a text that refer to the same entity. It has proven to be a useful intermediary step for a number of natural language processing applications. In this paper, we describe three implementations for performing coreference resolution: rule-based, statistical, and projectionbased (from English to German). After a comparative evaluation on benchmark datasets, we conclude with an application of these systems on German and English texts from different scenarios in digital curation such as an archive of personal letters, excerpts from a museum exhibition, and regional news articles.

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Multi-source annotation projection of coreference chains: assessing strategies and testing opportunities

Cited by 11 publications

References 12 publications

A Fine-grained Large-scale Analysis of Coreference Projection

A Fine-grained Large-scale Analysis of Coreference Projection

Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

Different German and English Coreference Resolution Models for Multi-domain Content Curation Scenarios

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