@inproceedings{jandaghi-pujara-2023-identifying,
    title = "Identifying Quantifiably Verifiable Statements from Text",
    author = "Jandaghi, Pegah  and
      Pujara, Jay",
    editor = "Hruschka, Estevam  and
      Mitchell, Tom  and
      Rahman, Sajjadur  and
      Mladeni{\'c}, Dunja  and
      Grobelnik, Marko",
    booktitle = "Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, ON, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.matching-1.2",
    doi = "10.18653/v1/2023.matching-1.2",
    pages = "14--22",
    abstract = "Humans often describe complex quantitative data using trend-based patterns. Trend-based patterns can be interpreted as higher order functions and relations over numerical data such as extreme values, rates of change, or cyclical repetition. One application where trends abound are descriptions of numerical tabular data. Therefore, the alignment of numerical tables and textual description of trends enables easier interpretations of tables. Most existing approaches can align quantities in text with tabular data but are unable to detect and align trend-based patterns about data. In this paper, we introduce the initial steps for aligning trend-based patterns about the data, i.e. the detection of textual description of trends and the alignment of trends with a relevant table. We introduce the problem of identifying quantifiably verifiable statements (QVS) in the text and aligning them with tables and datasets. We define the structure of these statements and implement a structured based detection. In our experiments, we demonstrate our method can detect and align these statements from several domains and compare favorably with traditional sequence labeling methods.",
}

Humans often describe complex quantitative data using trend-based patterns. Trend-based patterns can be interpreted as higher order functions and relations over numerical data such as extreme values, rates of change, or cyclical repetition. One application where trends abound are descriptions of numerical tabular data. Therefore, the alignment of numerical tables and textual description of trends enables easier interpretations of tables. Most existing approaches can align quantities in text with tabular data but are unable to detect and align trend-based patterns about data. In this paper, we introduce the initial steps for aligning trend-based patterns about the data, i.e. the detection of textual description of trends and the alignment of trends with a relevant table. We introduce the problem of identifying quantifiably verifiable statements (QVS) in the text and aligning them with tables and datasets. We define the structure of these statements and implement a structured based detection. In our experiments, we demonstrate our method can detect and align these statements from several domains and compare favorably with traditional sequence labeling methods.
J aghi, Pegah Pujara, Jay
Identifying Quantifiably Verifiable Statements from Text