metadata
license: cc-by-4.0
configs:
- config_name: split_0
data_files:
- split: train
path: split_0/train-*
- split: validation
path: split_0/validation-*
- split: test
path: split_0/test-*
- config_name: split_1
data_files:
- split: train
path: split_1/train-*
- split: validation
path: split_1/validation-*
- split: test
path: split_1/test-*
- config_name: split_2
data_files:
- split: train
path: split_2/train-*
- split: validation
path: split_2/validation-*
- split: test
path: split_2/test-*
- config_name: split_3
data_files:
- split: train
path: split_3/train-*
- split: validation
path: split_3/validation-*
- split: test
path: split_3/test-*
- config_name: split_4
data_files:
- split: train
path: split_4/train-*
- split: validation
path: split_4/validation-*
- split: test
path: split_4/test-*
- config_name: split_5
data_files:
- split: train
path: split_5/train-*
- split: validation
path: split_5/validation-*
- split: test
path: split_5/test-*
- config_name: split_6
data_files:
- split: train
path: split_6/train-*
- split: validation
path: split_6/validation-*
- split: test
path: split_6/test-*
- config_name: split_7
data_files:
- split: train
path: split_7/train-*
- split: validation
path: split_7/validation-*
- split: test
path: split_7/test-*
- config_name: split_8
data_files:
- split: train
path: split_8/train-*
- split: validation
path: split_8/validation-*
- split: test
path: split_8/test-*
- config_name: split_9
data_files:
- split: train
path: split_9/train-*
- split: validation
path: split_9/validation-*
- split: test
path: split_9/test-*
LitBank
- Project: https://github.com/dbamman/litbank
- Data source: https://github.com/dbamman/litbank/commit/3e50db0ffc033d7ccbb94f4d88f6b99210328ed8
- Crossval splits source: https://github.com/dbamman/lrec2020-coref/commit/e30de53743d36d1ea2c9e7292c69477fa332713c
Details
Ten configs of the form f"split_{X}" where X is in range(10)
Features
{'coref_chains': List[List[List[int]]] # list of clusters, each cluster is a list of mentions, each mention is a list of [sent_idx, start, end] inclusive
'doc_name': str
'entities': List[List[{'bio_tags': List[str]
'token': str}]], # list of sentences, each sentence is a list of tokens, each token has a list of bio tags and the token
'events': List[List[{'is_event': bool,
'token': str}]], # list of sentences, each sentence is a list of tokens, each token contains is_event and the token
'meta_info': {'author': str,
'date': str,
'gutenberg_id': str,
'title': str},
'original_text': str,
'quotes': List[{'attribution': str,
'end': {'sent_id': str,
'token_id': str},
'quotation': str,
'quote_id': str,
'start': {'sent_id': str,
'token_id': str}}],
'sentences': List[List[str]],
}
Citation
@inproceedings{bamman-etal-2019-annotated,
title = "An annotated dataset of literary entities",
author = "Bamman, David and
Popat, Sejal and
Shen, Sheng",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1220",
doi = "10.18653/v1/N19-1220",
pages = "2138--2144",
abstract = "We present a new dataset comprised of 210,532 tokens evenly drawn from 100 different English-language literary texts annotated for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle). These categories include non-named entities (such as {``}the boy{''}, {``}the kitchen{''}) and nested structure (such as [[the cook]{'}s sister]). In contrast to existing datasets built primarily on news (focused on geo-political entities and organizations), literary texts offer strikingly different distributions of entity categories, with much stronger emphasis on people and description of settings. We present empirical results demonstrating the performance of nested entity recognition models in this domain; training natively on in-domain literary data yields an improvement of over 20 absolute points in F-score (from 45.7 to 68.3), and mitigates a disparate impact in performance for male and female entities present in models trained on news data.",
}
Event detection
@inproceedings{sims-etal-2019-literary,
title = "Literary Event Detection",
author = "Sims, Matthew and
Park, Jong Ho and
Bamman, David",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1353",
doi = "10.18653/v1/P19-1353",
pages = "3623--3634",
abstract = "In this work we present a new dataset of literary events{---}events that are depicted as taking place within the imagined space of a novel. While previous work has focused on event detection in the domain of contemporary news, literature poses a number of complications for existing systems, including complex narration, the depiction of a broad array of mental states, and a strong emphasis on figurative language. We outline the annotation decisions of this new dataset and compare several models for predicting events; the best performing model, a bidirectional LSTM with BERT token representations, achieves an F1 score of 73.9. We then apply this model to a corpus of novels split across two dimensions{---}prestige and popularity{---}and demonstrate that there are statistically significant differences in the distribution of events for prestige.",
}
Coreference
@inproceedings{bamman-etal-2020-annotated,
title = "An Annotated Dataset of Coreference in {E}nglish Literature",
author = "Bamman, David and
Lewke, Olivia and
Mansoor, Anya",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.6",
pages = "44--54",
abstract = "We present in this work a new dataset of coreference annotations for works of literature in English, covering 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922. This dataset differs from previous coreference corpora in containing documents whose average length (2,105.3 words) is four times longer than other benchmark datasets (463.7 for OntoNotes), and contains examples of difficult coreference problems common in literature. This dataset allows for an evaluation of cross-domain performance for the task of coreference resolution, and analysis into the characteristics of long-distance within-document coreference.",
language = "English",
ISBN = "979-10-95546-34-4",
}