The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain

• URL: http://arxiv.org/abs/2006.03039v1
• Date: Thu, 4 Jun 2020 17:49:34 GMT
• Title: The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
• Authors: Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange
• Abstract summary: We develop an annotation scheme for marking information on experiments related to solid oxide fuel cells in scientific publications. A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested named entity recognition. We present strong neural-network based models for a variety of tasks that can be addressed on the basis of our new data set.
• Score: 11.085048329202335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related to solid oxide fuel cells in scientific publications, such as involved materials and measurement conditions. With this paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus consisting of 45 open-access scholarly articles annotated by domain experts. A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested named entity recognition and slot filling tasks as well as high annotation quality. We also present strong neural-network based models for a variety of tasks that can be addressed on the basis of our new data set. On all tasks, using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network on top seems beneficial. Our models will serve as competitive baselines in future work, and analysis of their performance highlights difficult cases when modeling the data and suggests promising research directions.

Related papers

• Artificial Intuition: Efficient Classification of Scientific Abstracts [42.299140272218274]
  Short scientific texts efficiently transmit dense information to experts possessing a rich body of knowledge to aid interpretation. To address this gap, we have developed a novel approach to generate and appropriately assign coarse domain-specific labels. We show that a Large Language Model (LLM) can provide metadata essential to the task, in a process akin to the augmentation of supplemental knowledge.
  arXiv  Detail & Related papers  (2024-07-08T16:34:47Z)
• MMSci: A Multimodal Multi-Discipline Dataset for PhD-Level Scientific Comprehension [59.41495657570397]
  We collected a multimodal, multidisciplinary dataset from open-access scientific articles published in Nature Communications journals. This dataset spans 72 scientific disciplines, ensuring both diversity and quality. We created benchmarks with various tasks and settings to comprehensively evaluate LMMs' capabilities in understanding scientific figures and content.
  arXiv  Detail & Related papers  (2024-07-06T00:40:53Z)
• Agent-based Learning of Materials Datasets from Scientific Literature [0.0]
  We develop a chemist AI agent, powered by large language models (LLMs), to create structured datasets from natural language text. Our chemist AI agent, Eunomia, can plan and execute actions by leveraging the existing knowledge from decades of scientific research articles.
  arXiv  Detail & Related papers  (2023-12-18T20:29:58Z)
• CARE: Extracting Experimental Findings From Clinical Literature [29.763929941107616]
  This work presents CARE, a new IE dataset for the task of extracting clinical findings. We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities and attributes. We collect extensive annotations for 700 abstracts from two sources: clinical trials and case reports.
  arXiv  Detail & Related papers  (2023-11-16T10:06:19Z)
• All Data on the Table: Novel Dataset and Benchmark for Cross-Modality Scientific Information Extraction [39.05577374775964]
  We propose a semi-supervised pipeline for annotating entities in text, as well as entities and relations in tables, in an iterative procedure. We release novel resources for the scientific community, including a high-quality benchmark, a large-scale corpus, and a semi-supervised annotation pipeline.
  arXiv  Detail & Related papers  (2023-11-14T14:22:47Z)
• MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain [0.7947524927438001]
  We present MuLMS, a new dataset of 50 open-access articles, spanning seven sub-domains of materials science. We present competitive neural models for all tasks and demonstrate that multi-task training with existing related resources leads to benefits.
  arXiv  Detail & Related papers  (2023-10-24T07:23:46Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)
• Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis [96.53859361560505]
  We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information. KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based. Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
  arXiv  Detail & Related papers  (2022-01-13T08:25:53Z)
• Unsupervised Opinion Summarization with Content Planning [58.5308638148329]
  We show that explicitly incorporating content planning in a summarization model yields output of higher quality. We also create synthetic datasets which are more natural, resembling real world document-summary pairs. Our approach outperforms competitive models in generating informative, coherent, and fluent summaries.
  arXiv  Detail & Related papers  (2020-12-14T18:41:58Z)
• KILT: a Benchmark for Knowledge Intensive Language Tasks [102.33046195554886]
  We present a benchmark for knowledge-intensive language tasks (KILT) All tasks in KILT are grounded in the same snapshot of Wikipedia. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline.
  arXiv  Detail & Related papers  (2020-09-04T15:32:19Z)
• ENT-DESC: Entity Description Generation by Exploring Knowledge Graph [53.03778194567752]
  In practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. We introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. We propose a multi-graph structure that is able to represent the original graph information more comprehensively.
  arXiv  Detail & Related papers  (2020-04-30T14:16:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.