Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions

Maria Berger, Elizabeth Goldstein


Abstract
Word embeddings capture semantic meaning of individual words. How to bridge word-level linguistic knowledge with sentence-level language representation is an open problem. This paper examines whether sentence-level representations can be achieved by building a custom sentence database focusing on one aspect of a sentence’s meaning. Our three separate semantic aspects are whether the sentence: (1) communicates a causal relationship, (2) indicates that two things are correlated with each other, and (3) expresses information or knowledge. The three classifiers provide epistemic information about a sentence’s content.
Anthology ID:
2021.law-1.15
Volume:
Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
DMR | EMNLP | LAW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
139–150
Language:
URL:
https://aclanthology.org/2021.law-1.15
DOI:
10.18653/v1/2021.law-1.15
Bibkey:
Cite (ACL):
Maria Berger and Elizabeth Goldstein. 2021. Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions. In Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 139–150, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions (Berger & Goldstein, LAW 2021)
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PDF:
https://aclanthology.org/2021.law-1.15.pdf