Domain Adaptation of General Natural Language Processing Tools for a Patent Claim Visualization System

Andersson, Linda; Lupu, Mihai; Hanbury, Allan

doi:10.1007/978-3-642-41057-4_8

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Some previous research has also tried to improve the understanding of entities in the claims by linking them to the Description, where they are mentioned in the context of actual embodiments [34]. Finally, other previous work has visualized claims in a more structured way, e.g., through graphs [35] or trees [36]. Previous work on simplification through rephrasing is much more limited.…”

Section: Patent Simplificationmentioning

confidence: 99%

Creating a Silver Standard for Patent Simplification

Casola

Lavelli

Saggion

2023

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Patents are legal documents that aim at protecting inventions on the one hand and at making technical knowledge circulate on the other. Their complex style -a mix of legal, technical, and extremely vague language -makes their content hard to access for humans and machines and poses substantial challenges to the information retrieval community. This paper proposes an approach to automatically simplify patent text through rephrasing. Since no in-domain parallel simplification data exist, we propose a method to automatically generate a large-scale silver standard for patent sentences. To obtain candidates, we use a general-domain paraphrasing system; however, the process is error-prone and difficult to control. Thus, we pair it with proper filters and construct a cleaner corpus that can successfully be used to train a simplification system. Human evaluation of the synthetic silver corpus shows that it is considered grammatical, adequate, and contains simple sentences.

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Section: Patent Simplificationmentioning

confidence: 99%

Creating a Silver Standard for Patent Simplification

Casola

Lavelli

Saggion

2023

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Another option is to visualize them in a structured way. Andersson et al (2013), for example, obtain a connected graph of the claim content; each node contains a noun phrase (NP) and is linked through a verb, a preposition, or a discourse relation. Similarly, (Kang et al, 2018) constructs a graph for visualizing the patent content in the contest of an Information Retrieval pipeline.…”

Section: Bodymentioning

confidence: 99%

Summarization, Simplification, and Generation: The Case of Patents

Casola,

Lavelli

2021

Preprint

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We survey Natural Language Processing (NLP) approaches to summarizing, simplifying, and generating patents' text. While solving these tasks has important practical applications -given patents' centrality in the R&D processpatents' idiosyncrasies open peculiar challenges to the current NLP state of the art. This survey aims at a) describing patents' characteristics and the questions they raise to the current NLP systems, b) critically presenting previous work and its evolution, and c) drawing attention to directions of research in which further work is needed. To the best of our knowledge, this is the first survey of generative approaches in the patent domain.

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“…In the last decade, UDDA has been identified as a prominent part of machine learning advancement. It is already being applied in a variety of fields, including computer vision and natural language processing [23].Unsupervised deep transfer learning is a deep transfer learning that uses unannotated data from the target domain [24]. The authors in [25] introduce a generic unsupervised deep learning approach to train deep models without the need for manual label supervision, and use a strategy to learn the underlying class decision boundaries iterative.…”

Section: Introductionmentioning

confidence: 99%

Airplane Detection Based on Unsupervised Deep Domain Adaptation in Remote Sensing Images

Youssef

Lyaqini

Fakhar

et al. 2022

Preprint

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In this work, we use the detection and localization capabilities of the pre-trained Faster Region Convolutional Neuronal Network (Faster R-CNN) model including Resnet50 as the backbone of the architecture. Our model has been trained with a huge benchmark dataset Common Objects in Context (MSCOCO) as a source domain. The model pre-trained is used in Unsupervised Deep Domain adaptation (UDDA) for airplane detection and localization in Remote Sensing Image (RSI) as a domain target. We evaluate our proposed approach using images containing multi-objects (airplanes) on a different scale and types collected from the public dataset for Object Detection in Aerial Images (DOTA) and different airport images extracted from Google Earth. Extensive experiments in a Cloud environment reveal the usefulness of the proposed approach regarding score. UDDA algorthm proposed is a non deterministic machine learning approach for detecting the airplane in RSI.

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Domain Adaptation of General Natural Language Processing Tools for a Patent Claim Visualization System

Cited by 5 publications

References 11 publications

Creating a Silver Standard for Patent Simplification

Creating a Silver Standard for Patent Simplification

Summarization, Simplification, and Generation: The Case of Patents

Airplane Detection Based on Unsupervised Deep Domain Adaptation in Remote Sensing Images

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