SubjQA: A Dataset for Subjectivity and Review Comprehension
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Documents
- SUBJQA
Final published version, 1.32 MB, PDF document
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.
Original language | English |
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Title of host publication | SUBJQA: A Dataset for Subjectivity and Review Comprehension |
Publisher | Association for Computational Linguistics |
Publication date | 2020 |
Pages | 5480-5494 |
DOIs | |
Publication status | Published - 2020 |
Event | The 2020 Conference on Empirical Methods in Natural Language Processing - online Duration: 16 Nov 2020 → 20 Nov 2020 http://2020.emnlp.org |
Conference
Conference | The 2020 Conference on Empirical Methods in Natural Language Processing |
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Location | online |
Periode | 16/11/2020 → 20/11/2020 |
Internetadresse |
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