SubjQA: A Dataset for Subjectivity and Review Comprehension

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

SubjQA : A Dataset for Subjectivity and Review Comprehension. / Bjerva, Johannes; Bhutani, Nikita; Golshan, Behzad; Tan, Wang-chiew; Augenstein, Isabelle.

SUBJQA: A Dataset for Subjectivity and Review Comprehension. Association for Computational Linguistics, 2020. p. 5480-5494.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Bjerva, J, Bhutani, N, Golshan, B, Tan, W & Augenstein, I 2020, SubjQA: A Dataset for Subjectivity and Review Comprehension. in SUBJQA: A Dataset for Subjectivity and Review Comprehension. Association for Computational Linguistics, pp. 5480-5494, The 2020 Conference on Empirical Methods in Natural Language Processing, 16/11/2020. https://doi.org/10.18653/v1/2020.emnlp-main.442

APA

Bjerva, J., Bhutani, N., Golshan, B., Tan, W., & Augenstein, I. (2020). SubjQA: A Dataset for Subjectivity and Review Comprehension. In SUBJQA: A Dataset for Subjectivity and Review Comprehension (pp. 5480-5494). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.442

Vancouver

Bjerva J, Bhutani N, Golshan B, Tan W, Augenstein I. SubjQA: A Dataset for Subjectivity and Review Comprehension. In SUBJQA: A Dataset for Subjectivity and Review Comprehension. Association for Computational Linguistics. 2020. p. 5480-5494 https://doi.org/10.18653/v1/2020.emnlp-main.442

Author

Bjerva, Johannes ; Bhutani, Nikita ; Golshan, Behzad ; Tan, Wang-chiew ; Augenstein, Isabelle. / SubjQA : A Dataset for Subjectivity and Review Comprehension. SUBJQA: A Dataset for Subjectivity and Review Comprehension. Association for Computational Linguistics, 2020. pp. 5480-5494

Bibtex

@inproceedings{ba60aad4b6484c78b1b353ad7a82d1d0,
title = "SubjQA: A Dataset for Subjectivity and Review Comprehension",
abstract = "Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect of user-generated data. In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA). We develop a new dataset which allows us to investigate this relationship. We find that subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous work on sentiment analysis. For instance, a subjective question may or may not be associated with a subjective answer. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 domains.",
author = "Johannes Bjerva and Nikita Bhutani and Behzad Golshan and Wang-chiew Tan and Isabelle Augenstein",
year = "2020",
doi = "10.18653/v1/2020.emnlp-main.442",
language = "English",
pages = "5480--5494",
booktitle = "SUBJQA: A Dataset for Subjectivity and Review Comprehension",
publisher = "Association for Computational Linguistics",
note = "The 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 ; Conference date: 16-11-2020 Through 20-11-2020",
url = "http://2020.emnlp.org",

}

RIS

TY - GEN

T1 - SubjQA

T2 - The 2020 Conference on Empirical Methods in Natural Language Processing

AU - Bjerva, Johannes

AU - Bhutani, Nikita

AU - Golshan, Behzad

AU - Tan, Wang-chiew

AU - Augenstein, Isabelle

PY - 2020

Y1 - 2020

N2 - Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect of user-generated data. In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA). We develop a new dataset which allows us to investigate this relationship. We find that subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous work on sentiment analysis. For instance, a subjective question may or may not be associated with a subjective answer. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 domains.

AB - Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect of user-generated data. In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA). We develop a new dataset which allows us to investigate this relationship. We find that subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous work on sentiment analysis. For instance, a subjective question may or may not be associated with a subjective answer. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 domains.

U2 - 10.18653/v1/2020.emnlp-main.442

DO - 10.18653/v1/2020.emnlp-main.442

M3 - Article in proceedings

SP - 5480

EP - 5494

BT - SUBJQA: A Dataset for Subjectivity and Review Comprehension

PB - Association for Computational Linguistics

Y2 - 16 November 2020 through 20 November 2020

ER -

ID: 254991810