Patent Sentiment Analysis to Highlight Patent Paragraphs

• URL: http://arxiv.org/abs/2111.09741v1
• Date: Sat, 6 Nov 2021 13:28:29 GMT
• Title: Patent Sentiment Analysis to Highlight Patent Paragraphs
• Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, and Markus Endres
• Abstract summary: Given a patent document, identifying distinct semantic annotations is an interesting research aspect. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This work assist patent practitioners in highlighting semantic information automatically and aid to create a sustainable and efficient patent analysis using the aptitude of Machine Learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a novel dataset to train Machine Learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class, novel dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, iv) dataset and codes relating to this task are open-sourced through a dedicated GIT web page: https://github.com/Renuk9390/Patent_Sentiment_Analysis and v) future path to extend this work using Deep Learning and domain specific pre-trained language models to develop a tool to highlight is provided. This work assist patent practitioners in highlighting semantic information automatically and aid to create a sustainable and efficient patent analysis using the aptitude of Machine Learning.

Related papers

• Pap2Pat: Towards Automated Paper-to-Patent Drafting using Chunk-based Outline-guided Generation [13.242188189150987]
  We present PAP2PAT, a new challenging benchmark of 1.8k patent-paper pairs with document outlines. Our experiments with current open-weight LLMs and outline-guided generation show that they can effectively use information from the paper but struggle with repetitions, likely due to the inherent repetitiveness of patent language.
  arXiv  Detail & Related papers  (2024-10-09T15:52:48Z)
• A Comprehensive Survey on AI-based Methods for Patents [14.090575139188422]
  AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle. This interdisciplinary survey aims to serve as a resource for researchers and practitioners working at the intersection of AI and patent analysis.
  arXiv  Detail & Related papers  (2024-04-02T20:44:06Z)
• Natural Language Processing in Patents: A Survey [0.0]
  Patents, encapsulating crucial technical and legal information, present a rich domain for natural language processing (NLP) applications. As NLP technologies evolve, large language models (LLMs) have demonstrated outstanding capabilities in general text processing and generation tasks. This paper aims to equip NLP researchers with the essential knowledge to navigate this complex domain efficiently.
  arXiv  Detail & Related papers  (2024-03-06T23:17:16Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification [48.5140223214582]
  State-of-the-art methods for multi-label patent classification rely on deep opaque neural networks (DNNs) We propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class.
  arXiv  Detail & Related papers  (2023-10-31T14:11:37Z)
• 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)
• The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications [8.110699646062384]
  We introduce the Harvard USPTO Patent dataset (HUPD) With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora. By providing each application's metadata along with all of its text fields, the dataset enables researchers to perform new sets of NLP tasks.
  arXiv  Detail & Related papers  (2022-07-08T17:57:15Z)
• A Survey on Sentence Embedding Models Performance for Patent Analysis [0.0]
  We propose a standard library and dataset for assessing the accuracy of embeddings models based on PatentSBERTa approach. Results show PatentSBERTa, Bert-for-patents, and TF-IDF Weighted Word Embeddings have the best accuracy for computing sentence embeddings at the subclass level.
  arXiv  Detail & Related papers  (2022-04-28T12:04:42Z)
• MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images [49.664220687980006]
  The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models. We present MONAI Label, a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models.
  arXiv  Detail & Related papers  (2022-03-23T12:33:11Z)
• Combining Feature and Instance Attribution to Detect Artifacts [62.63504976810927]
  We propose methods to facilitate identification of training data artifacts. We show that this proposed training-feature attribution approach can be used to uncover artifacts in training data. We execute a small user study to evaluate whether these methods are useful to NLP researchers in practice.
  arXiv  Detail & Related papers  (2021-07-01T09:26:13Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)

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.