Papers by Huitong Pan
SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents (2024.emnlp-main)
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| Challenge: | Scientific information extraction (SciIE) is critical for converting unstructured knowledge from scholarly articles into structured data. |
| Approach: | They propose to use a scientific entity and relation extraction dataset to capture interactions between entities in full texts. |
| Outcome: | The proposed dataset captures the intricate use and interactions among entities in full texts and provides an out-of-distribution test set to offer a more realistic evaluation. |
DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition (2025.findings-naacl)
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| Challenge: | Existing methods to identify entities using distant annotations are expensive and time-consuming. |
| Approach: | They propose a training dynamics-based label cleaning approach to characterize distant annotations and an automatic threshold estimation strategy to locate errors in distant labels. |
| Outcome: | The proposed method outperforms several advanced DS-NER approaches across four datasets. |
Taxonomy-Driven Knowledge Graph Construction for Domain-Specific Scientific Applications (2025.findings-acl)
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| Challenge: | Existing methods for constructing domain-specific knowledge graphs neglect curated taxonomies and LLMs fail to extract KGs in specialized domains. |
| Approach: | They propose a taxonomy-driven framework for constructing domain-specific knowledge graphs . they use structured taxonomies, Large Language Models and Retrieval-Augmented Generation . |
| Outcome: | The proposed framework can be adapted for other specialized domains. |
SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions (2024.lrec-main)
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| Challenge: | SciDMT is an enhanced and expanded corpus for scientific mention detection . existing corpora are limited by their small volume and entity linking capabilities . |
| Approach: | They propose to enhance SciDMT, an annotated scientific corpus for scientific mention detection. |
| Outcome: | The proposed corpus is the largest for scientific entity mention detection . it is based on deep learning architectures like SciBERT and GPT-3.5 . |
DMDD: A Large-Scale Dataset for Dataset Mentions Detection (2023.tacl-1)
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| Challenge: | Existing corpora for dataset mention detection are limited in size and naming diversity. |
| Approach: | They propose a dataset for dataset mention detection that is the largest publicly available corpus for this task. |
| Outcome: | The proposed dataset is the largest publicly available corpus for dataset mention detection . it identifies open problems in dataset mention recognition and linking . |