# A Zipf's Law-based Text Generation Approach for Addressing Imbalance in Entity Extraction

arXiv: https://arxiv.org/abs/2205.12636 · HTML: https://mlsanity.com/paper/2205.12636/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 9 of 9)

- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Chinese NER Using Lattice LSTM](https://mlsanity.com/paper/1805.02023/) (2018)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain](https://mlsanity.com/paper/2203.04729/) (2022)
- [Incremental Text-to-Speech Synthesis Using Pseudo Lookahead with Large Pretrained Language Model](https://mlsanity.com/paper/2012.12612/) (2020)
- [Using the Full-text Content of Academic Articles to Identify and Evaluate Algorithm Entities in the Domain of Natural Language Processing](https://mlsanity.com/paper/2010.10817/) (2020)
- [A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process](https://mlsanity.com/paper/2111.13854/) (2021)
- [Why KDAC? A general activation function for knowledge discovery](https://mlsanity.com/paper/2111.13858/) (2021)

## Built on by (most cited first, 2 of 2)

- [LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods](https://mlsanity.com/paper/2407.00322/) (2024)
- [Will sentiment analysis need subculture? A new data augmentation approach](https://mlsanity.com/paper/2309.00178/) (2023)

## Abstract (arXiv)

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.