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  1. Book ; Online: Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism

    Yoo, Yongmin / Heo, Tak-Sung / Lim, Dongjin / Seo, Deaho

    2023  

    Abstract: Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence- ... ...

    Abstract Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence-related patents are very difficult to classify because it is a mixture of complex technologies and legal terms. Moreover, due to the unsatisfactory performance of current algorithms, it is still mostly done manually, wasting a lot of time and money. Therefore, we present a method for classifying artificial intelligence-related patents published by the USPTO using natural language processing technique and deep learning methodology. We use deformed BERT and sentence attention overcome the limitations of BERT. Our experiment result is highest performance compared to other deep learning methods.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 401 ; 006
    Publishing date 2023-03-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Book ; Online: Solar cell patent classification method based on keyword extraction and deep neural network

    Yoo, Yongmin / Lim, Dongjin / Heo, Tak-Sung

    2021  

    Abstract: With the growing impact of ESG on businesses, research related to renewable energy is receiving great attention. Solar cells are one of them, and accordingly, it can be said that the research value of solar cell patent analysis is very high. Patent ... ...

    Abstract With the growing impact of ESG on businesses, research related to renewable energy is receiving great attention. Solar cells are one of them, and accordingly, it can be said that the research value of solar cell patent analysis is very high. Patent documents have high research value. Being able to accurately analyze and classify patent documents can reveal several important technical relationships. It can also describe the business trends in that technology. And when it comes to investment, new industrial solutions will also be inspired and proposed to make important decisions. Therefore, we must carefully analyze patent documents and utilize the value of patents. To solve the solar cell patent classification problem, we propose a keyword extraction method and a deep neural network-based solar cell patent classification method. First, solar cell patents are analyzed for pretreatment. It then uses the KeyBERT algorithm to extract keywords and key phrases from the patent abstract to construct a lexical dictionary. We then build a solar cell patent classification model according to the deep neural network. Finally, we use a deep neural network-based solar cell patent classification model to classify power patents, and the training accuracy is greater than 95%. Also, the validation accuracy is about 87.5%. It can be seen that the deep neural network method can not only realize the classification of complex and difficult solar cell patents, but also have a good classification effect.
    Keywords Computer Science - Information Retrieval ; Computer Science - Computation and Language
    Subject code 006
    Publishing date 2021-09-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Book ; Online: A novel hybrid methodology of measuring sentence similarity

    Yoo, Yongmin / Heo, Tak-Sung / Park, Yeongjoon

    2021  

    Abstract: The problem of measuring sentence similarity is an essential issue in the natural language processing (NLP) area. It is necessary to measure the similarity between sentences accurately. There are many approaches to measuring sentence similarity. Deep ... ...

    Abstract The problem of measuring sentence similarity is an essential issue in the natural language processing (NLP) area. It is necessary to measure the similarity between sentences accurately. There are many approaches to measuring sentence similarity. Deep learning methodology shows a state-of-the-art performance in many natural language processing fields and is used a lot in sentence similarity measurement methods. However, in the natural language processing field, considering the structure of the sentence or the word structure that makes up the sentence is also important. In this study, we propose a methodology combined with both deep learning methodology and a method considering lexical relationships. Our evaluation metric is the Pearson correlation coefficient and Spearman correlation coefficient. As a result, the proposed method outperforms the current approaches on a KorSTS standard benchmark Korean dataset. Moreover, it performs a maximum of 65% increase than only using deep learning methodology. Experiments show that our proposed method generally results in better performance than those with only a deep learning model.
    Keywords Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2021-05-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Book ; Online: A Novel Patent Similarity Measurement Methodology

    Yoo, Yongmin / Jeong, Cheonkam / Gim, Sanguk / Lee, Junwon / Schimke, Zachary / Seo, Deaho

    Semantic Distance and Technological Distance

    2023  

    Abstract: Patent similarity analysis plays a crucial role in evaluating the risk of patent infringement. Nonetheless, this analysis is predominantly conducted manually by legal experts, often resulting in a time-consuming process. Recent advances in natural ... ...

    Abstract Patent similarity analysis plays a crucial role in evaluating the risk of patent infringement. Nonetheless, this analysis is predominantly conducted manually by legal experts, often resulting in a time-consuming process. Recent advances in natural language processing technology offer a promising avenue for automating this process. However, methods for measuring similarity between patents still rely on experts manually classifying patents. Due to the recent development of artificial intelligence technology, a lot of research is being conducted focusing on the semantic similarity of patents using natural language processing technology. However, it is difficult to accurately analyze patent data, which are legal documents representing complex technologies, using existing natural language processing technologies. To address these limitations, we propose a hybrid methodology that takes into account bibliographic similarity, measures the similarity between patents by considering the semantic similarity of patents, the technical similarity between patents, and the bibliographic information of patents. Using natural language processing techniques, we measure semantic similarity based on patent text and calculate technical similarity through the degree of coexistence of International patent classification (IPC) codes. The similarity of bibliographic information of a patent is calculated using the special characteristics of the patent: citation information, inventor information, and assignee information. We propose a model that assigns reasonable weights to each similarity method considered. With the help of experts, we performed manual similarity evaluations on 420 pairs and evaluated the performance of our model based on this data. We have empirically shown that our method outperforms recent natural language processing techniques.
    Keywords Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence ; Computer Science - Social and Information Networks
    Subject code 303
    Publishing date 2023-03-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Book ; Online: Medical Code Prediction from Discharge Summary

    Heo, Tak-Sung / Yoo, Yongmin / Park, Yeongjoon / Jo, Byeong-Cheol

    Document to Sequence BERT using Sequence Attention

    2021  

    Abstract: Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the international classification of diseases (ICD). ICD code is an important code used in a ... ...

    Abstract Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the international classification of diseases (ICD). ICD code is an important code used in a variety of operations, including insurance, reimbursement, medical diagnosis, etc. Therefore, it is important to classify ICD codes quickly and accurately. However, annotating these codes is costly and time-consuming. So we propose a model based on bidirectional encoder representations from transformer (BERT) using the sequence attention method for automatic ICD code assignment. We evaluate our ap-proach on the MIMIC-III benchmark dataset. Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555, and is performing better than a performance of the previous state-of-the-art model. The contribution of this study proposes a method of using BERT that can be applied to documents and a sequence attention method that can capture im-portant sequence information appearing in documents.
    Keywords Computer Science - Artificial Intelligence
    Subject code 005
    Publishing date 2021-06-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Book ; Online: DAGAM

    Jo, Byeong-Cheol / Heo, Tak-Sung / Park, Yeongjoon / Yoo, Yongmin / Cho, Won Ik / Kim, Kyungsun

    Data Augmentation with Generation And Modification

    2022  

    Abstract: Text classification is a representative downstream task of natural language processing, and has exhibited excellent performance since the advent of pre-trained language models based on Transformer architecture. However, in pre-trained language models, ... ...

    Abstract Text classification is a representative downstream task of natural language processing, and has exhibited excellent performance since the advent of pre-trained language models based on Transformer architecture. However, in pre-trained language models, under-fitting often occurs due to the size of the model being very large compared to the amount of available training data. Along with significant importance of data collection in modern machine learning paradigm, studies have been actively conducted for natural language data augmentation. In light of this, we introduce three data augmentation schemes that help reduce underfitting problems of large-scale language models. Primarily we use a generation model for data augmentation, which is defined as Data Augmentation with Generation (DAG). Next, we augment data using text modification techniques such as corruption and word order change (Data Augmentation with Modification, DAM). Finally, we propose Data Augmentation with Generation And Modification (DAGAM), which combines DAG and DAM techniques for a boosted performance. We conduct data augmentation for six benchmark datasets of text classification task, and verify the usefulness of DAG, DAM, and DAGAM through BERT-based fine-tuning and evaluation, deriving better results compared to the performance with original datasets.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 004 ; 006
    Publishing date 2022-04-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Article ; Online: A near-zero Poisson's ratio of Si with ordered nanopores.

    Yoo, Yongmin / Kim, Young-Joo / Kim, Do-Nyun / Lee, Joo-Hyoung

    Physical chemistry chemical physics : PCCP

    2016  Volume 18, Issue 31, Page(s) 21949–21953

    Abstract: The Poisson's ratio νij = -ε/ε, where ε and ε (i,j = x, y, z) are applied and resulting strain, respectively, are computed from first-principles for Si with an array of cylindrical, nanometer-sized pores aligned in the z direction (nanoporous Si, or np- ... ...

    Abstract The Poisson's ratio νij = -ε/ε, where ε and ε (i,j = x, y, z) are applied and resulting strain, respectively, are computed from first-principles for Si with an array of cylindrical, nanometer-sized pores aligned in the z direction (nanoporous Si, or np-Si). Through density functional theory calculations, it is demonstrated that the periodic arrangement of pores introduces strong anisotropy in the Poisson's ratio of np-Si: while νyz remains close to the Poisson's ratio of the bulk, νzx and νxy exhibit an increase and a sharp decrease from the bulk value, respectively, as the volume fraction of pores (ϕ) becomes large. It is shown that the characteristic dependence of the Poisson's ratio on ϕ originates from the difference in the actual stress on np-Si, which is caused by the dissimilar surface geometry. Unlike random porous materials, this finding signifies the importance of structural details in determining the mechanical response of ordered systems at a nanoscale.
    Language English
    Publishing date 2016-08-03
    Publishing country England
    Document type Journal Article
    ZDB-ID 1476244-4
    ISSN 1463-9084 ; 1463-9076
    ISSN (online) 1463-9084
    ISSN 1463-9076
    DOI 10.1039/c6cp03248f
    Database MEDical Literature Analysis and Retrieval System OnLINE

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