Concise summaries of recent research papers on AI patent search to assist you in your research endeavors. - researchpapersummary artificialintelligence
Laborious, expensive, and exhaustive are a few adjectives that come to mind when describing the traditional ways of processing patent data, whether it is classifying patents, clustering documents, or highlighting paragraphs. However, bright minds like yourself are actively coming up with research to automate many such activities using artificial intelligence.
So, as a researcher, you always want to stay abreast of the latest developments in AI patent search. Just as the greatest ever ice hockey player,In this piece, we will share concise summaries of recent research papers on AI patent search to assist you in your research endeavors. Let’s learn more about them.
PatentNet: Multi-label Classification of patent documents using deep learning-based language understandingThe researchers attempted to automate complicated, laborious, and expensive patent classification tasks such as multi-label classification using finely tuned pre-trained language models such as BERT, XLNet, RoBERTa, and ELECTRA. Next, the models underwent comparison against the baseline deep-learning approaches CNN, LSTM, BiLSTM, and CNN-BiLSTM.
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