An Empirical Analysis of Edit Importance between Document Versions

Goyal, Tanya; Kelkar, Sachin; Agarwal, Manas; Grover, Jeenu

doi:10.18653/v1/d17-1295

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…There is some prior work on automatically analyzing edits between document versions. Some have focused on classifying edits in Wikipedia articles (Bronner and Monz, 2012;Daxenberger andGurevych, 2013), andGoyal et al (2017) measured the importance of different kinds of changes between versions of news articles. The EIS documents we analyze have a very different semantics to their versioning.…”

Section: Related Workmentioning

confidence: 99%

Inferring missing metadata from environmental policy texts

Bethard¹,

Laparra²,

Wang³

et al. 2019

Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities An

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The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years. Unfortunately, there is no central database for this information and it is too voluminous to assess manually. We describe our efforts to enable systematic research over US environmental policy by extracting and organizing metadata from the text of NEPA documents. Our contributions include collecting more than 40,000 NEPA-related documents, and evaluating rule-based baselines that establish the difficulty of three important tasks: identifying lead agencies, aligning document versions, and detecting reused text.

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Section: Related Workmentioning

confidence: 99%

Inferring missing metadata from environmental policy texts

Bethard¹,

Laparra²,

Wang³

et al. 2019

Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities An

View full text Add to dashboard Cite

show abstract

“…There is some prior work on automatically analyzing edits between document versions. Some have focused on classifying edits in Wikipedia articles (Bronner and Monz, 2012; Daxenberger and Gurevych, 2013), and Goyal et al (2017) measured the importance of different kinds of changes between versions of news articles. The EIS documents we analyze have a very different semantics to their versioning.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

2019

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Welcome to the third edition of LaTeCH-CLfL-which also is the thirteenth edition of LaTeCH and eighth of CLfL. We have had fun preparing the workshop, and we will be happy if you have fun attending (or at least reading the workshop papers:). Please visit the website at https://sighum.wordpress.com/events/latech-clfl-2019/ where you will find the workshop presentations, among other things. The papers cover, as usual, topics which you will not easily find at regular NLP conferences. The authors take on literary texts, including drama and poetry, and more generally literary study; historical texts; ancient or otherwise old languages; government documents; code switching; and more. Last but certainly not least, we will have an invited talk. Ian Milligan, a historian, has a deep interest in Digital Humanities, and understands the role on Natural Language Processing in his discipline. It is our pleasant duty to thank the authors: there would be no workshop without you. Nor without the program committee, to whom we are ever so grateful for their thorough and helpful reviews.

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Characterizing Text Revisions to Better Support Collaborative

Ping

Verspoor²,

Miller

2022

2022 International Conference on Digital Transformation and Intelligence (ICDI)

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Despite advancement in collaborative writing tools, the track changes capability in modern editors remains limited to highlighting syntactic changes, with authors still required to manually read through each of the revisions. We envision a collaborative authoring system where an author could accept all minor edits first and then focus on the substantial changes. To support this, we define the task of significant revision identification as the task of identifying the revisions between two versions of a text according to one of four categories, i.e. formal, meaning preserving, micro-and macro-structure. Microstructure change corresponds to minor meaning change while macro-structure change corresponds to major meaning change. Our main contribution is to define a computational approach to this task, by framing the task as bi-directional entailment between the original and revised sentences. An existing recognition of textual entailment (RTE) system is applied to evaluate whether the revised texts entails. We evaluate the approach through a novel corpus consisting of multiple versions of drafts of academic papers written by multiple authors, which were annotated with the four revision types by both authors and non-authors of the papers. The proposed bi-directional textual entailment approach performs better than baseline edit distance approaches, which is similar to the current track changes capability built into most word processors.

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An Empirical Analysis of Edit Importance between Document Versions

Cited by 3 publications

References 5 publications

Inferring missing metadata from environmental policy texts

Inferring missing metadata from environmental policy texts

Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

Characterizing Text Revisions to Better Support Collaborative

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