  Entity extraction is critical in the intelligent advancement across diverse
domains. Nevertheless, a challenge to its effectiveness arises from the data
imbalance. This paper proposes a novel approach by viewing the issue through
the quantitative information, recognizing that entities exhibit certain levels
of commonality while others are scarce, which can be reflected in the
quantifiable distribution of words. The Zipf's Law emerges as a well-suited
adoption, and to transition from words to entities, words within the documents
are classified as common and rare ones. Subsequently, sentences are classified
into common and rare ones, and are further processed by text generation models
accordingly. Rare entities within the generated sentences are then labeled
using human-designed rules, serving as a supplement to the raw dataset, thereby
mitigating the imbalance problem. The study presents a case of extracting
entities from technical documents, and experimental results from two datasets
prove the effectiveness of the proposed method. Furthermore, the significance
of Zipf's law in driving the progress of AI is discussed, broadening the reach
and coverage of Informetrics. This paper presents a successful demonstration of
extending Informetrics to interface with AI through Zipf's Law.
