This paper introduces our system for the SemEval 2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) competition. Our team (KDDIE) focused on the sub-task of Named Entity Recognition (NER) for the language of English in the competition 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.

KDDIE at SemEval-2023 Task 2: External Knowledge Injection for Named Entity Recognition

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.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-demo.7/)


PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature

We present a novel approach to developing an end-to-end learning framework to segment and classify major regions of a scientific document. We consider scientific document layout analysis as an object detection task over digital images, without any additional text features that need to be added into the network during the training process. Our technical objective is to implement transfer learning via fine-tuning of pre-trained networks and thereby demonstrate that this deep learning architecture is suitable for tasks that lack very large document corpora for training ab initio. As part of the experimental test bed for empirical evaluation of this approach, we created a merged multi-corpus data set for scientific publication layout detection tasks. Our results show good improvement with fine-tuning of a pre-trained base network using this merged data set, compared to the baseline convolutional neural network architecture.

Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks

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Huichen Yang

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Presentations

KDDIE at SemEval-2023 Task 2: External Knowledge Injection for Named Entity Recognition

PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature

Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks

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