Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark

Xu, Zhenran; Shan, Zifei; Li, Yuxin; Hu, Baotian; Qin, Bing

doi:10.1145/3539597.3570418

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Recently, generative pretrained transformers such as Llama-2 21 and GPT-4 22 have been trained and applied to clinical NLP tasks in zero-shot or few-shot settings, which brings new inspirations to our system design. 23,24 Integrating various NLP tools into a unified pipeline poses significant challenges, notably managing component dependencies and system stability. Errors in early stages can affect downstream performance, evident from declines seen from named entity recognition to relation normalization.…”

Section: Discussionmentioning

confidence: 99%

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition

Zuo,

Kumar,

Shen

et al. 2024

JCO Clin Cancer Inform

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PURPOSE The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning–/deep learning–based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

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Section: Discussionmentioning

confidence: 99%

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition

Zuo,

Kumar,

Shen

et al. 2024

JCO Clin Cancer Inform

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“…In 2023, Zhenran Xu et al [12] launched Hansel, a unique Chinese entity linking benchmark. While it addresses the challenge of linking uncommon or emerging entities, top systems struggle with this task.…”

Section: Embedding-based Approaches For Entity Linkingmentioning

confidence: 99%

Entity Linking Method for Chinese Short Texts with Multiple Embedded Representations

Shi

Yin

et al. 2023

Electronics

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Entity linking, a crucial task in the realm of natural language processing, aims to link entity mentions in a text to their corresponding entities in the knowledge base. While long documents provide abundant contextual information, facilitating feature extraction for entity identification and disambiguation, entity linking in Chinese short texts presents significant challenges. This study introduces an innovative approach to entity linking within Chinese short texts, combining multiple embedding representations. It integrates embedding representations from both entities and relations in the knowledge graph triples, as well as embedding representations from the descriptive text of entities and relations, to enhance the performance of entity linking. The method also incorporates external semantic supplements to strengthen the model’s feature learning capabilities. The Multi-Embedding Representation–Bidirectional Encoder Representation from Transformers–Bidirectional Gated Recurrent Unit (MER-BERT-BiGRU) neural network model is employed for embedding learning. The precision, recall, and F1 scores reached 89.73%, 92.18%, and 90.94% respectively, demonstrating the effectiveness of our approach.

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Improving Few-Shot and Zero-Shot Entity Linking with Coarse-to-Fine Lexicon-Based Retriever

Huang,

Wang,

Qin

et al. 2023

Lecture Notes in Computer Science

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Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark

Cited by 6 publications

References 19 publications

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition

Entity Linking Method for Chinese Short Texts with Multiple Embedded Representations

Improving Few-Shot and Zero-Shot Entity Linking with Coarse-to-Fine Lexicon-Based Retriever

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