William Hsu


2023

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KDDIE at SemEval-2023 Task 2: External Knowledge Injection for Named Entity Recognition
Caleb Martin | Huichen Yang | William Hsu
Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper introduces our system for the SemEval 2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) competition. Our team focused on the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. To achieve our goal, we utilized transfer learning by fine-tuning pre-trained language models (PLMs) on the competition dataset. Our approach involved combining a BERT-based PLM with external knowledge to provide additional context to the model. In this report, we present our findings and results.

2022

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KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition
Caleb Martin | Huichen Yang | William Hsu
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition. Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset. Our two approaches are the BERT-based PLMs and PLMs with additional layer such as Condition Random Field. We report our finding and results in this report.

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PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature
Huichen Yang | Carlos Aguirre | William Hsu
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations

The published materials science literature contains abundant description information about synthesis procedures that can help discover new material areas, deepen the study of materials synthesis, and accelerate its automated planning. Nevertheless, this information is expressed in unstructured text, and manually processing and assimilating useful information is expensive and time-consuming for researchers. To address this challenge, we develop a Machine Learning-based procedural information extraction and knowledge management system (PIEKM) that extracts procedural information recipe steps, figures, and tables from materials science articles, and provides information retrieval capability and the statistics visualization functionality. Our system aims to help researchers to gain insights and quickly understand the connections among massive data. Moreover, we demonstrate that the machine learning-based system performs well in low-resource scenarios (i.e., limited annotated data) for domain adaption.

2010

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KSU KDD: Word Sense Induction by Clustering in Topic Space
Wesam Elshamy | Doina Caragea | William Hsu
Proceedings of the 5th International Workshop on Semantic Evaluation