A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction

TL;DR

This paper introduces a new Chinese dataset that includes negative full forms for abbreviation prediction, addressing a gap in existing corpora and enabling better model training for general abbreviation tasks.

Contribution

The paper creates and releases a Chinese abbreviation dataset with negative full forms, facilitating research on abbreviation prediction including non-abbreviable expressions.

Findings

Evaluated multiple models on the dataset

Dataset improves the study of negative full forms

Baseline results provided for future research

Abstract

Abbreviation is a common phenomenon across languages, especially in Chinese. In most cases, if an expression can be abbreviated, its abbreviation is used more often than its fully expanded forms, since people tend to convey information in a most concise way. For various language processing tasks, abbreviation is an obstacle to improving the performance, as the textual form of an abbreviation does not express useful information, unless it's expanded to the full form. Abbreviation prediction means associating the fully expanded forms with their abbreviations. However, due to the deficiency in the abbreviation corpora, such a task is limited in current studies, especially considering general abbreviation prediction should also include those full form expressions that do not have valid abbreviations, namely the negative full forms (NFFs). Corpora incorporating negative full forms for general abbreviation prediction are few in number. In order to promote the research in this area, we build a dataset for general Chinese abbreviation prediction, which needs a few preprocessing steps, and evaluate several different models on the built dataset. The dataset is available at https://github.com/lancopku/Chinese-abbreviation-dataset