Enhancing Named Entity Recognition in the Chemical Domain via Metric Learning and Pseudo-Labeling

Основні поняття

A model is proposed to effectively transfer knowledge from the biomedical domain to the chemical domain for named entity recognition, by projecting source and target entities into separate regions of the feature space.

Анотація

The paper investigates the applicability of transfer learning for enhancing a named entity recognition (NER) model trained in the biomedical domain (the source domain) to be used in the chemical domain (the target domain). The authors observe that a common practice of pretraining the model on labeled source data and then finetuning it on a few labeled target examples is prone to mislabeling source entities as target entities, due to the shared context between the two domains.

To address this issue, the authors propose a two-stage model:

1. Entity Grouping in the Source Domain:

   • Extracting auxiliary information about source entities from annotated events to establish relations between entities.
   • Employing a refined multi-similarity loss to group similar source entities and project them into separate regions of the feature space.

2. Entity Discrimination in the Target Domain:

   • Detecting potentially false positive entities (i.e., source entities) using pseudo-labeling.
   • Leveraging the multi-similarity loss to enhance discrimination between the pseudo-labeled entities and the target entities, projecting them into separate regions of the feature space.

The authors conduct extensive experiments across three source and three target datasets, demonstrating that their method outperforms the baselines by up to 5% in absolute F1 score. They also provide detailed empirical analysis to shed light on the effectiveness of the proposed techniques.

Статистика

The catechol and hydroquinone metabolites, induced apoptosis in HL60 and HBMP cells in a time- and concentration dependent manner.
The phenols, NCR181, FLA873, and FLA797, and the derivatives formed by oxidation of the pyrrolidine ring had no effect.

Цитати

"Named entity recognition is a crucial step in IE tasks. Existing models have achieved remarkable performance in the general domain (Lin et al., 2020; Wang et al., 2021b; Zhang et al., 2023; Shen et al., 2023b). However, in the scientific domains, e.g., medical or chemical domains, these models usually struggle due to the extremely large quantity of concepts, the wide presence of multi-token entities, and the ambiguity in detecting entity boundaries."
"Given the already existing challenges of the named entity recognition task in the scientific domain—mentioned earlier—this factor can further exacerbate the problem. For instance, in our early few-shot learning experiments, we observed that the results of ChatGPT in the chemical domain named entity recognition task are significantly worse than those in the general domain."

Ключові висновки, отримані з

Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences

by Hongyi Liu,Q... о arxiv.org 04-02-2024

https://arxiv.org/pdf/2401.10472.pdf

Глибші Запити

How can the proposed framework be extended to handle nested entities or overlapping entities across the source and target domains?

In order to handle nested entities or overlapping entities across different domains, the proposed framework can be extended by incorporating more sophisticated entity representation techniques. One approach could involve utilizing hierarchical models that can capture nested structures within entities. For nested entities, the model can be designed to recognize the hierarchical relationships between entities and their sub-entities. This can be achieved by incorporating specialized tokenization schemes or encoding strategies that can differentiate between different levels of entities.
To address overlapping entities, the model can be enhanced to have a more nuanced understanding of entity boundaries. This can involve implementing advanced token-level labeling mechanisms that can distinguish between overlapping entities based on contextual cues. Additionally, the model can be trained to assign probabilities to different entity spans, allowing for more flexible recognition of overlapping entities.

How can the proposed framework be extended to handle nested entities or overlapping entities across the source and target domains?

In addition to event annotations, the model can leverage other types of external knowledge to further enhance its performance. One potential source of external knowledge could be domain-specific ontologies or knowledge graphs. By incorporating structured information from these resources, the model can gain a deeper understanding of entity relationships and domain-specific concepts. This can help improve entity recognition accuracy and facilitate better knowledge transfer between domains.
Furthermore, domain-specific lexicons or dictionaries can be utilized to enrich the model's vocabulary and improve entity recognition. By incorporating domain-specific terminology and semantic relationships, the model can better capture the nuances of the target domain and enhance its ability to recognize entities accurately.

How would the proposed approach perform on other domain pairs beyond biomedical and chemical, where the shared context between the domains is less pronounced?

The proposed approach could be applied to other domain pairs beyond biomedical and chemical, even when the shared context between the domains is less pronounced. In such cases, the model may face greater challenges in transferring knowledge due to the dissimilarity between the domains. However, the framework's ability to project entities into separate regions of the feature space can still be beneficial in distinguishing between source and target entities.
To adapt the approach to domains with less shared context, additional pretraining on a broader range of data or incorporating domain adaptation techniques may be necessary. By fine-tuning the model on a more diverse set of source and target data, the model can learn to generalize across different domains more effectively. Additionally, incorporating domain-specific features or external knowledge sources relevant to the new domains can help improve the model's performance in transferring knowledge and recognizing entities accurately.