Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification

• URL: http://arxiv.org/abs/2308.05385v2
• Date: Wed, 19 Jun 2024 02:45:02 GMT
• Title: Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification
• Authors: Tao Zou, Le Yu, Junchen Ye, Leilei Sun, Bowen Du, Deqing Wang,
• Abstract summary: We propose an integrated framework that comprehensively considers the information on patents for patent classification. We first present an IPC codes correlations learning module to derive their semantic representations. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions.
• Score: 26.85734804493925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Recent methods for automatically classifying patents mainly focus on analyzing the text descriptions of patents. However, apart from the texts, each patent is also associated with some assignees, and the knowledge of their applied patents is often valuable for classification. Furthermore, the hierarchical taxonomy formulated by the IPC system provides important contextual information and enables models to leverage the correlations between IPC codes for more accurate classification. However, existing methods fail to incorporate the above aspects. In this paper, we propose an integrated framework that comprehensively considers the information on patents for patent classification. To be specific, we first present an IPC codes correlations learning module to derive their semantic representations via adaptively passing and aggregating messages within the same level and across different levels along the hierarchical taxonomy. Moreover, we design a historical application patterns learning component to incorporate the corresponding assignee's previous patents by a dual channel aggregation mechanism. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions. Experiments on real-world datasets demonstrate the superiority of our approach over the existing methods. Besides, we present the model's ability to capture the temporal patterns of assignees and the semantic dependencies among IPC codes.

Related papers

• FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs [50.13829380113614]
  We propose a novel framework that augments Large Vision-Language Models with forgery-specific knowledge for manipulation reasoning. FakeNewsGPT4 achieves superior cross-domain performance compared to previous methods.
  arXiv  Detail & Related papers  (2024-03-04T12:35:09Z)
• PaECTER: Patent-level Representation Learning using Citation-informed Transformers [0.16785092703248325]
  PaECTER is a publicly available, open-source document-level encoder specific for patents. We fine-tune BERT for Patents with examiner-added citation information to generate numerical representations for patent documents. PaECTER performs better in similarity tasks than current state-of-the-art models used in the patent domain.
  arXiv  Detail & Related papers  (2024-02-29T18:09:03Z)
• Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition [49.536368818512116]
  Tactics, Techniques and Procedures (TTPs) represent sophisticated attack patterns in the cybersecurity domain. We formulate the problem in a different learning paradigm, where the assignment of a text to a TTP label is decided by the direct semantic similarity between the two. We propose a neural matching architecture with an effective sampling-based learn-to-compare mechanism.
  arXiv  Detail & Related papers  (2024-01-18T19:02:00Z)
• 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)
• Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction [45.0907126466271]
  We propose an event-based graph learning framework for patent application trend prediction. In particular, our method is founded on the memorable representations of both companies and patent classification codes.
  arXiv  Detail & Related papers  (2023-08-04T05:43:32Z)
• Description-Enhanced Label Embedding Contrastive Learning for Text Classification [65.01077813330559]
  Self-Supervised Learning (SSL) in model learning process and design a novel self-supervised Relation of Relation (R2) classification task. Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets. external knowledge from WordNet to obtain multi-aspect descriptions for label semantic learning.
  arXiv  Detail & Related papers  (2023-06-15T02:19:34Z)
• 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)
• Automated Single-Label Patent Classification using Ensemble Classifiers [0.0]
  We present an innovative method of ensemble classifiers trained with different parts of the patent document. To the best of our knowledge, this is the first time that an ensemble method was proposed for the patent classification problem.
  arXiv  Detail & Related papers  (2022-03-03T08:47:15Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)

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.