Papers by Diya Li
Estimating Agreement by Chance for Sequence Annotation (2024.acl-long)
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| Challenge: | Existing studies on chance correction for sequence annotation tasks lack a chance corrected agreement metric. |
| Approach: | They propose a model for generating random annotations which serves as the foundation for estimating chance agreement in sequence annotation tasks. |
| Outcome: | The proposed model is validated in simulation and corpus-based evaluation. |
Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction (D19-62)
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| Challenge: | 80% of the data sets for relation extraction tasks are negative instances, resulting in a lack of syntactic information between two entity mentions. |
| Approach: | They propose a graph convolutional networks model that incorporates dependency parsing and contextualized embedding to capture comprehensive contextual information. |
| Outcome: | The proposed model achieves state-of-the-art F-score on the 2013 drug-drug interaction extraction task. |
Food Knowledge Representation Learning with Adversarial Substitution (2022.aacl-main)
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| Challenge: | Knowledge graph embedding (KGE) has been well-studied in general domains, but has not been examined for food computing. |
| Approach: | They propose to use a pre-trained language model to encode entities and relations, emphasizing contextual information in food KGs. |
| Outcome: | The proposed method is able to generate high quality substitutions over a food knowledge graph and provide generalized substitutions to meet different user needs. |
Automated Clinical Data Extraction with Knowledge Conditioned LLMs (2025.coling-industry)
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| Challenge: | Large language models (LLMs) can be effective at interpreting unstructured text in reports, but they often hallucinate due to a lack of domain-specific knowledge. |
| Approach: | They propose a framework that aligns generated internal knowledge with external knowledge through in-context learning (ICL) they use a retriever to identify relevant units of internal or external knowledge and a grader to evaluate the truthfulness and usefulness of the retrieved internal-knowledge rules to align and update the knowledge bases. |
| Outcome: | Experiments with expert-curated test datasets show that the proposed framework can increase the F1 score for key fields by 12.9% over existing methods. |
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM (N19-1)
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| Challenge: | Biomedical event extraction requires domain-specific knowledge and deep understanding of complex contexts. |
| Approach: | They propose a knowledge base-driven tree-structured long short-term memory networks framework . tree-LSTM framework incorporates dependency structures and entity properties from ontologies . |
| Outcome: | The proposed framework is based on the BioNLP shared task with Genia dataset and achieves state-of-the-art results. |