Extracting Numeric Assertions from Text

ACL ARR 2025 July Submission505 Authors

Abstract:

Open-domain Information Extraction (IE) plays an essential role in constructing large-scale knowledge bases and supports downstream applications such as Question Answering, Text Summarization, etc.
While most prior research in IE has centered around extracting categorical relational tuples (e.g., president of, located in), the extraction of numerical relations (e.g., literacy rate, area, molecular weight), that link quantitative mentions to corresponding entities, remains relatively under-explored. This work addresses this gap by targeting the extraction of open-domain numeric assertions, which require identifying both the relevant entity and the appropriate measuring attribute associated with a quantity in natural language text. We begin by refining an existing OpenIE system through a rule-based approach where retrieving implicit measuring attributes for a quantity mention becomes the main challenge. To overcome this, we propose a neural framework that jointly identifies the relevant entity for a numeric mention and infers the measuring attribute to relate them, using contextual cues in the sentence. Experimental evaluation shows that our proposed model outperforms the baseline and a general-purpose large language model with a significantly large margin.

Paper Type: Long

Research Area: Information Extraction

Research Area Keywords: open information extraction, fine-tuning, Multi-task learning

Contribution Types: Publicly available software and/or pre-trained models, Data resources, Theory

Languages Studied: English

Submission Number: 505