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Patent Semantic Annotation for Practitioners

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dc.contributor.author Kabir, Summiya
dc.date.accessioned 2023-08-25T10:09:41Z
dc.date.available 2023-08-25T10:09:41Z
dc.date.issued 2023
dc.identifier.other 318255
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/37539
dc.description Supervisor: Dr. Muhammad Khuram Shahzad en_US
dc.description.abstract Having a patent document, associating discrete semantic annotations has emerged as an exciting research area. Text annotation aids patent practitioners, such as; examiners and attorneys in quickly identifying the important arguments of any invention, allowing for quick marking of the patent text. In the manual patent analysis process, recognizing the semantic information by marking paragraphs is commonly used to improve readabil ity. To overcome this time-consuming and laborious task, we have used Deep Learning models on the dataset of size 150k patent samples. Prior researchers have used differ ent Machine Learning models but we approached this problem as deep learning task and investigate the merits of Deep learning models for highlighting patent paragraphs. To raise the level of performance of the base models of Patent Sentiment Analysis, we trained five different deep learning-based models on the Patent dataset: Simple Feed forward Neural Network, Long Short-Term Memory (LSTM), BERT, RoBERTa, and DistilBERT. We have trained different Feed Forward Neural Networks (FFNN) with different hyperparameters and logged our precision, recall and f1-score. We used pre trained GloVe embeddings as wordembeddings. Sentence embeddings were calculated as the element-wise mean of all word vectors of that sentence. We have trained FFNN for 500 epochs and log the loss and test the accuracy of the model after each epoch. The loss and accuracy graphs are smoothed using exponential moving averages so we can see the overall trend of the model. This work uses Deep Learning to assist patent prac titioners in automatically highlighting semantic information and creating a sustainable and efficient patent analysis. en_US
dc.language.iso en en_US
dc.publisher School of Electrical Engineering and Computer Sciences (SEECS), NUST en_US
dc.title Patent Semantic Annotation for Practitioners en_US
dc.type Thesis en_US


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