Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition (2024)

Abstract: While the abundance of rich and vast datasets across numerous fields has facilitated the advancement of natural language processing, sectors in need of specialized data types continue to struggle with the challenge of finding quality data. Our study introduces a novel guidance data augmentation technique utilizing abstracted context and sentence structures to produce varied sentences while maintaining context-entity relationships, addressing data scarcity challenges. By fostering a closer relationship between context, sentence structure, and role of entities, our method enhances data augmentation’s effectiveness. Consequently, by showcasing diversification in both entity-related vocabulary and overall sentence structure, and simultaneously improving the training performance of named entity recognition task.

## introduction the field of natural language processing (nlp) has witnessed remarkable success across various domains in recent years, primarily attributed to the availability of rich and high-quality data. however, specialized fields such as science and biology face significant challenges due to the scarcity of such quality data. particularly, tasks like named entity recognition (ner) face significant difficulties due to domain-specific characteristics where vocabulary roles diverge from general usage, necessitating specialized knowledge for effective data collection. to overcome the data shortage issue, various automated data augmentation (da) techniques have been developed, including a recent approach that leverages large language models (llms) for sentence generation to perform da @xcite . utilizing llms for da involves employing few-shot learning or external modules @xcite to provide additional information. in ner tasks, da is applied with a focus on entities, maintaining the sentence's core structure with in this study, we propose guidance data augmentation (gda), utilizing information on context and sentence structure abstracted through data abstraction for da, aiming to generate sentences with varied structures alongside augmenting similar entity types. this approach seeks to achieve more natural and diverse da compared to single llm methods by augmenting data with sentences of varied structures that match the seed sentence's context and corresponding entity types. our data abstraction approach structures relationships among context, entities, and sentence composition for da, expanding inference scope by using higher-level conceptual information @xcite . this approach is vital where entity-related terms diverge significantly from general usage, re-given the sentence : the presence of a moving object and easily estimable gravity vector. augment the 3 sentences by replacing the entity tokens with new ones of the same types. ## related works in nlp, widely used da techniques comprise rulebased approaches such as synonym replacement, back-translation, and random text element insertion or deletion. these methods are especially prevalent in tasks where textual data may require diversification to better train models @xcite . specifically, for tasks such as ner, augmentation strategies often revolve around the substitution of words with similar meanings or roles. in this context, techniques like easy data augmentation (eda) @xcite and the utilization of wordnet @xcite through the natural language toolkit (nltk) @xcite are frequently applied to generate synonyms-based augmented data. in particular, when using data from specialized domain, da methods are often used because it is difficult to collect data as it often consists of data containing domain-specific knowledge. for biomedical named entity recognition, augmentation is often performed using context to enhance the understanding of specialized concepts @xcite . recently, the utilization of llms has expanded, leading to an increased use of da techniques based on llms. these techniques involve augmenting data for sentence classification by leveraging llms @xcite , or enhancing cross-lingual tasks @xcite through augmentation. ## guidance data augmentation our framework is designed around two key components for effective da aimed at ner tasks: data abstraction and data augmentation via guidance prompts. through these two approaches, the proposed method facilitates enhanced model performance on ner tasks. ## conclusion in this study, we proposed guidance data augmentation designed for ner tasks within specific domain data, enabling the generation of data suited for these tasks. by using data abstraction, our method facilitates structured relationships among context, entities, and sentence composition, allowing for the generation of sentences with diverse structures while ensuring entity consistency. the abstracted sentence information is utilized in constructing guidance prompts, enabling da with a rich diversity in vocabulary and sentence structures. future efforts will aim at refining this process for applicability to additional tasks and exploring the use of multiple guidance llms to enrich the abstraction information, thereby enhancing the guidance provided.