Polymers are widely used in diverse fields, and the demand for efficient methods to extract and organize information about them is increasing. An automated approach that utilizes machine learning can accurately extract relevant information from scientific papers, providing a promising solution for automating information extraction using annotated training data. In this paper, we introduce a polymer-relevant ontology featuring crucial entities and relations to enhance information extraction in the polymer science field. Our ontology is customizable to adapt to specific research needs. We present PolyNERE, a high-quality named entity recognition (NER) and relation extraction (RE) corpus comprising 750 polymer abstracts annotated using our ontology. Distinctive features of PolyNERE include multiple entity types, relation categories, support for various NER settings, and the ability to assert entities and relations at different levels. PolyNERE also facilitates reasoning in the RE task through supporting evidence. While our experiments with recent advanced methods achieved promising results, challenges persist in adapting NER and RE from abstracts to full-text paragraphs. This emphasizes the need for robust information extraction systems in the polymer domain, making our corpus a valuable benchmark for future developments.

Recently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based Recurrent Neural Networks (DepRNNs). While SACNNs allow the model to selectively focus on the important information segment from the raw sequence, DepRNNs help to handle the long-distance relations from the shortest dependency path of relation entities. Experiments on the SemEval-2010 Task 8 dataset show that our model is comparable to the state-of-the-art without using any external lexical features.

This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction. We first point out that these tasks are related. Then, inspired by ranking relation instances and patterns computed by the HITS algorithm, and selecting cluster centroids using the K-means, LSA, or NMF method, we propose methods for selecting the initial seeds from an existing resource, or reducing the level of noise in the distantly labeled data. Experiments show that our proposed methods achieve a better performance than the baseline systems in both tasks.