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UD English GUM

Language: English (code: en)
Family: IE

This treebank has been part of Universal Dependencies since the UD v2.2 release.

The following people have contributed to making this treebank part of UD: Siyao Peng, Amir Zeldes.

Repository: UD_English-GUM
Search this treebank on-line: PML-TQ
Download all treebanks: UD 2.15

License: CC BY-NC-SA 4.0

Genre: academic, blog, email, fiction, government, legal, news, nonfiction, social, spoken, web, wiki

Questions, comments? General annotation questions (either English-specific or cross-linguistic) can be raised in the main UD issue tracker. You can report bugs in this treebank in the treebank-specific issue tracker on Github. If you want to collaborate, please contact [amir • zeldes (æt) georgetown • edu]. Development of the treebank happens outside the UD repository. If there are bugs, either the original data source or the conversion procedure must be fixed. Do not submit pull requests against the UD repository.

Annotation Source
Lemmas annotated manually
UPOS annotated manually in non-UD style, automatically converted to UD
XPOS annotated manually
Features annotated manually in non-UD style, automatically converted to UD
Relations annotated manually, natively in UD style

Description

Universal Dependencies syntax annotations from the GUM corpus (https://gucorpling.org/gum/)

GUM, the Georgetown University Multilayer corpus, is an open source collection of richly annotated texts from multiple text types. The corpus is collected and expanded by students as part of the curriculum in the course LING-4427 “Computational Corpus Linguistics” at Georgetown University. The selection of text types is meant to represent different communicative purposes, while coming from sources that are readily and openly available (usually Creative Commons licenses), so that new texts can be annotated and published with ease.

The dependencies in the corpus up to GUM version 5 were originally annotated using Stanford Typed Depenencies (de Marneffe & Manning 2013) and converted automatically to UD using DepEdit (https://gucorpling.org/depedit/). The rule-based conversion took into account gold annotations found in other annotation layers of the GUM corpus (e.g. entity annotations), and has since been corrected manually in native UD. The original conversion script used can found in the GUM build bot code from version 5, available from the (non-UD) GUM repository. Documents from version 6 of GUM onwards were annotated directly in UD, and subsequent manual error correction to all GUM data has also been done directly using the UD guidelines. Enhanced dependencies were added semi-automatically from version 7.1 of the corpus. For more details see the corpus website.

Acknowledgments

GUM annotation team (so far - thanks for participating!)

Adrienne Isaac, Akitaka Yamada, Alex Giorgioni, Alexandra Berends, Alexandra Slome, Amani Aloufi, Amber Hall, Amelia Becker, Andrea Price, Andrew O’Brien, Ángeles Ortega Luque, Aniya Harris, Anna Prince, Anna Runova, Anne Butler, Arianna Janoff, Aryaman Arora, Ayan Mandal, Aysenur Sagdic, Bertille Baron, Bradford Salen, Brandon Tullock, Brent Laing, Caitlyn Pineault, Calvin Engstrom, Candice Penelton, Carlotta Hübener, Caroline Gish, Charlie Dees, Chenyue Guo, Chloe Evered, Cindy Luo, Colleen Diamond, Connor O’Dwyer, Cristina Lopez, Cynthia Li, Dan DeGenaro, Dan Simonson, Derek Reagan, Devika Tiwari, Didem Ikizoglu, Edwin Ko, Eliza Rice, Emile Zahr, Emily Pace, Emma Manning, Emma Rafkin, Ethan Beaman, Felipe De Jesus, Han Bu, Hana Altalhi, Hang Jiang, Hannah Wingett, Hanwool Choe, Hassan Munshi, Helen Dominic, Ho Fai Cheng, Hortensia Gutierrez, Jakob Prange, James Maguire, Janine Karo, Jehan al-Mahmoud, Jemm Excelle Dela Cruz, Jess Godes, Jessica Cusi, Jessica Kotfila, Jingni Wu, Joaquin Gris Roca, John Chi, Jongbong Lee, Juliet May, Jungyoon Koh, Katarina Starcevic, Katelyn Carroll, Katelyn MacDougald, Katherine Vadella, Khalid Alharbi, Kristen Cook, Lara Bryfonski, Lauren Levine, Leah Northington, Lindley Winchester, Linxi Zhang, Lucia Donatelli, Luke Gessler, Mackenzie Gong, Margaret Anne Rowe, Margaret Borowczyk, Maria Laura Zalazar, Maria Stoianova, Mariko Uno, Mary Henderson, Maya Barzilai, Md. Jahurul Islam, Michael Kranzlein, Michaela Harrington, Mingyeong Choi, Minnie Annan, Mitchell Abrams, Mohammad Ali Yektaie, Naomee-Minh Nguyen, Negar Siyari, Nicholas Mararac, Nicholas Workman, Nicole Steinberg, Nitin Venkateswaran, Parker DiPaolo, Phoebe Fisher, Rachel Kerr, Rachel Thorson, Rebecca Childress, Rebecca Farkas, Riley Breslin Amalfitano, Rima Elabdali, Robert Maloney, Ruizhong Li, Ryan Mannion, Ryan Murphy, Sakol Suethanapornkul, Sarah Bellavance, Sarah Carlson, Sasha Slone, Saurav Goswami, Sean Macavaney, Sean Simpson, Seyma Toker, Shane Quinn, Shannon Mooney, Shelby Lake, Shira Wein, Sichang Tu, Siddharth Singh, Siona Ely, Siyao Peng, Siyu Liang, Stephanie Kramer, Sylvia Sierra, Talal Alharbi, Tatsuya Aoyama, Tess Feyen, Timothy Ingrassia, Trevor Adriaanse, Ulie Xu, Wai Ching Leung, Wenxi Yang, Wesley Scivetti, Xiaopei Wu, Xiulin Yang, Yang Liu, Yi-Ju Lin, Yifu Mu, Yilun Zhu, Yingzhu Chen, Yiran Xu, Young-A Son, Yu-Tzu Chang, Yuhang Hu, Yunjung Ku, Yushi Zhao, Zhijie Song, Zhuosi Luo, Zhuxin Wang, Amir Zeldes

… and other annotators who wish to remain anonymous!

References

The best paper to cite depends on the data you are using. To cite the corpus in general, please refer to the following article (but note that the corpus has changed and grown a lot in the time since); otherwise see different citations for specific aspects below:

Zeldes, Amir (2017) “The GUM Corpus: Creating Multilayer Resources in the Classroom”. Language Resources and Evaluation 51(3), 581–612.

@Article{Zeldes2017,
author = {Amir Zeldes},
title = {The {GUM} Corpus: Creating Multilayer Resources in the Classroom},
journal = {Language Resources and Evaluation},
year = {2017},
volume = {51},
number = {3},
pages = {581--612},
doi = {http://dx.doi.org/10.1007/s10579-016-9343-x}
}

If you are using the Reddit subset of GUM in particular, please use this citation instead:

@InProceedings{BehzadZeldes2020,
author = {Shabnam Behzad and Amir Zeldes},
title = {A Cross-Genre Ensemble Approach to Robust {R}eddit Part of Speech Tagging},
booktitle = {Proceedings of the 12th Web as Corpus Workshop (WAC-XII)},
pages = {50--56},
year = {2020},
}

For papers focusing on the discourse relations, discourse markers or other discourse signal annotations, please cite the eRST paper:

@misc{ZeldesEtAl2024,
title={{eRST}: A Signaled Graph Theory of Discourse Relations and Organization},
author={Amir Zeldes and Tatsuya Aoyama and Yang Janet Liu and Siyao Peng and Debopam Das and Luke Gessler},
year={2024},
eprint={2403.13560},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2403.13560}
}

For papers using GDTB/PDTB style shallow discourse relations, please cite:

@inproceedings{liu-etal-2024-GDTB,
title = "GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains",
author = "Yang Janet Liu and Tatsuya Aoyama and Wesley Scivetti and Yilun Zhu and Shabnam Behzad and Lauren Elizabeth Levine and Jessica Lin and Devika Tiwari and Amir Zeldes",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, USA",
publisher = "Association for Computational Linguistics",
abstract = "Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted to the news domain, and is by now 35 years old. In this paper, we present and evaluate a new open-access, multi-genre benchmark for PDTB-style shallow discourse parsing, based on the existing UD English GUM corpus, for which discourse relation annotations in other frameworks already exist. In a series of experiments on cross-domain relation classification, we show that while our dataset is compatible with PDTB, substantial out-of-domain degradation is observed, which can be alleviated by joint training on both datasets.",
}

If you are using the OntoNotes schema version of the coreference annotations (a.k.a. OntoGUM data in coref/ontogum/), please cite this paper instead:

@InProceedings{ZhuEtAl2021,
author = {Yilun Zhu and Sameer Pradhan and Amir Zeldes},
booktitle = {Proceedings of ACL-IJCNLP 2021},
title = {{OntoGUM}: Evaluating Contextualized {SOTA} Coreference Resolution on 12 More Genres},
year = {2021},
pages = {461--467},
address = {Bangkok, Thailand}

For papers focusing on named entities or entity linking (Wikification), please cite this paper instead:

@inproceedings{lin-zeldes-2021-wikigum,
title = {{W}iki{GUM}: Exhaustive Entity Linking for Wikification in 12 Genres},
author = {Jessica Lin and Amir Zeldes},
booktitle = {Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and
3rd Designing Meaning Representations (DMR) Workshop (LAW-DMR 2021)},
year = {2021},
address = {Punta Cana, Dominican Republic},
url = {https://aclanthology.org/2021.law-1.18},
pages = {170--175},
}

Statistics of UD English GUM

POS Tags

ADJ – ADP – ADV – AUX – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – SYM – VERB – X

Features

Abbr – Case – Definite – Degree – ExtPos – Foreign – Gender – Mood – Number – NumForm – NumType – Person – Polarity – Poss – PronType – Reflex – Style – Tense – Typo – VerbForm – Voice

Relations

acl – acl:relcl – advcl – advcl:relcl – advmod – amod – appos – aux – aux:pass – case – cc – cc:preconj – ccomp – compound – compound:prt – conj – cop – csubj – csubj:outer – csubj:pass – dep – det – det:predet – discourse – dislocated – expl – fixed – flat – goeswith – iobj – list – mark – nmod – nmod:poss – nmod:unmarked – nsubj – nsubj:outer – nsubj:pass – nummod – obj – obl – obl:agent – obl:unmarked – orphan – parataxis – punct – reparandum – root – vocative – xcomp

Tokenization and Word Segmentation

Morphology

Tags

Nominal Features

Degree and Polarity

Verbal Features

Pronouns, Determiners, Quantifiers

Other Features

Syntax

Auxiliary Verbs and Copula

Core Arguments, Oblique Arguments and Adjuncts

Here we consider only relations between verbs (parent) and nouns or pronouns (child).

Verbs with Reflexive Core Objects

Relations Overview