Patent document clustering with deep embeddings

Yoon, Janghyeok; Park, Eunjeong L.; Choi, Sungchul

doi:10.1007/s11192-020-03396-7

Cited by 35 publications

(14 citation statements)

References 20 publications

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“…A way of detection of the topics within fields of science is to extract topics from textual data, using either topic modeling approaches (Jelodar et al, 2019;Xu, Zhai, et al, 2019;Zhou et al, 2019) or clustering approaches (Curiskis et al, 2020;Kim et al, 2020;Radu et al, 2020;. The overall process, considering the clustering approach in this study can be simplified and put into stages including Document Embedding, Document categorization and clustering, and Labeling of the categories, illustrated in Figure 1.…”

Section: Review Of Topic Detection Processes and Approachesmentioning

confidence: 99%

“…DEC is a deep neural networks based method designed for clustering, leveraging dimensionality reduction power of the neural networks. Kim et al (2020) in a patent document clustering study, utilizes DEC. They use Doc2Vec (Le & Mikolov, 2014) to acquire the patent document vectors, then feed the document vectors to an autoencoder as the pre-training stage.…”

Section: Combined Document Embedding and Clusteringmentioning

confidence: 99%

“…Thus, choosing the appropriate dataset is a crucial step to correctly validate the methodology. One of the suitable datasets for the task of this study is KIPRIS dataset  ; a dataset of patent documents having 5 categories, also compiled and used in (Kim et al, 2020). The dataset comprises 19,710 patents.…”

Section: Da Tasetmentioning

confidence: 99%

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Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Vahidnia

Abbasi

Abbass

2021

Journal of Data and Information Science

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Purpose Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific fields. This also helps in having a better collaboration with governments and businesses. This study aims to investigate the development of research fields over time, translating it into a topic detection problem. Design/methodology/approach To achieve the objectives, we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic documents. Document embedding approaches are utilized to transform documents into vector-based representations. The proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms (i.e. LDA) against a benchmark dataset. A case study is also conducted exploring the evolution of Artificial Intelligence (AI) detecting the research topics or sub-fields in related AI publications. Findings Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark dataset. Using the proposed method, we also show how the topics have evolved in the period of the recent 30 years, taking advantage of a keyword extraction method for cluster tagging and labeling, demonstrating the context of the topics. Research limitations We noticed that it is not possible to generalize one solution for all downstream tasks. Hence, it is required to fine-tune or optimize the solutions for each task and even datasets. In addition, interpretation of cluster labels can be subjective and vary based on the readers’ opinions. It is also very difficult to evaluate the labeling techniques, rendering the explanation of the clusters further limited. Practical implications As demonstrated in the case study, we show that in a real-world example, how the proposed method would enable the researchers and reviewers of the academic research to detect, summarize, analyze, and visualize research topics from decades of academic documents. This helps the scientific community and all related organizations in fast and effective analysis of the fields, by establishing and explaining the topics. Originality/value In this study, we introduce a modified and tuned deep embedding clustering coupled with Doc2Vec representations for topic extraction. We also use a concept extraction method as a labeling approach in this study. The effectiveness of the method has been evaluated in a case study of AI publications, where we analyze the AI topics during the past three decades.

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Section: Review Of Topic Detection Processes and Approachesmentioning

confidence: 99%

Section: Combined Document Embedding and Clusteringmentioning

confidence: 99%

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Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Vahidnia

Abbasi

Abbass

2021

Journal of Data and Information Science

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“…Traditionally, data clustering is an unsupervised learning task, meaning that the number of clusters is unknown, and none of the input data points are labeled. Applications of clustering include image segmentation [3], text mining [4], gene expression analysis [5], air pollution analysis [6], and fault diagnosis [7], to name only a few.…”

Section: Introductionmentioning

confidence: 99%

“…Indices with set distance δ 5 are omitted; see[75] for a definition 4. https://cran.r-project.org/web/packages/clusterCrit 5 https://cran.r-project.org/web/packages/clv8 VOLUME ?…”

mentioning

confidence: 99%

Weighted Cluster Ensemble Based on Partition Relevance Analysis With Reduction Step

Ilc

2020

IEEE Access

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Over the last decade, the advent of the cluster ensemble framework has enabled more accurate and robust data analysis than traditional single clustering algorithms. The improved clustering of microarray data has had a particularly strong impact in the fields of genomics and medicine. However, when we bring several ensemble members together to form a consensus, low-quality data partitions can seriously compromise the final solution. One way to overcome this problem is the weighted cluster ensemble approach based on Partition Relevance Analysis (PRA), which uses internal cluster validity indices to evaluate and weight the ensemble members before the fusion. Unfortunately, the selection of appropriate validation indices for given data is far from trivial. In this paper, we propose an additional step in PRA that reduces the size of the committee of cluster validation indices. It does so by eliminating redundant and noisy indices using data dimensionality reduction methods. Our extension works in an unsupervised way, minimizing the amount of user intervention and required expert knowledge. We adapted three conventional consensus functions based on the principle of evidence accumulation to work with PRA weights. We demonstrate the advantages of the proposed reduction step of PRA based on extensive experiments with 25 gene expression and 15 non-genetic real-world datasets, where we compared 15 consensus functions.

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Identifying the Trends of Technological Convergence Between Domains Using a Heterogeneous Graph Perspective: A Case Study of the Graphene Industry

Jiang,

Meng,

Zhou

2024

Lecture Notes in Computer Science

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Patent document clustering with deep embeddings

Cited by 35 publications

References 20 publications

Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Weighted Cluster Ensemble Based on Partition Relevance Analysis With Reduction Step

Identifying the Trends of Technological Convergence Between Domains Using a Heterogeneous Graph Perspective: A Case Study of the Graphene Industry

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