Who Cited It

Question Answering For Toxicological Information Extraction

2022 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 1,569 citations · 5 from inside this corpus

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Working with large amounts of text data has become hectic and time-consuming. In order to reduce human effort, costs, and make the process more efficient, companies and organizations resort to intelligent algorithms to automate and assist the manual work. This problem is also present in the field of toxicological analysis of chemical substances, where information needs to be searched from multiple documents. That said, we propose an approach that relies on Question Answering for acquiring information from unstructured data, in our case, English PDF documents containing information about physicochemical and toxicological properties of chemical substances. Experimental results confirm that our approach achieves promising results which can be applicable in the business scenario, especially if further revised by humans.

Question Answering For Toxicological Information Extraction (2022)Question Answering For Toxico…Transformers: State-of-the-Art Natural Language Processing (2020)Transformers: State-of-the-Ar…CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020)CodeBERT: A Pre-Trained Model…ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning (2021)ProtTrans: Toward Understandi…TinyBERT: Distilling BERT for Natural Language Understanding (2020)TinyBERT: Distilling BERT for…Deep Learning--based Text Classification (2021)Deep Learning--based Text Cla…
5 of 5 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What cites it, inside the corpus

PaperYearCited
Transformers: State-of-the-Art Natural Language Processing20208,295
CodeBERT: A Pre-Trained Model for Programming and Natural Languages20202,630
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning20212,446
TinyBERT: Distilling BERT for Natural Language Understanding20201,706
Deep Learning--based Text Classification20211,525

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Topics

Topic ModelingComputer Science
Natural Language Processing TechniquesComputer Science
Machine Learning and Data ClassificationComputer Science

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