@inproceedings{berger-goldstein-2021-increasing,
    title = "Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions",
    author = "Berger, Maria  and
      Goldstein, Elizabeth",
    editor = "Bonial, Claire  and
      Xue, Nianwen",
    booktitle = "Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.law-1.15/",
    doi = "10.18653/v1/2021.law-1.15",
    pages = "139--150",
    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."
}