A deep learning based method for extracting semantic information from patent documents

Chen, Liang; Xu, Shuo; Zhu, Liehuang; Zhang, Jing; Lei, Xiaoping; Yang, Guancan

doi:10.1007/s11192-020-03634-y

Cited by 54 publications

(31 citation statements)

References 37 publications

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“…Unfortunately, the performance of CNN model is not satisfactory. In our opinion, the data sets for identifying important citations are small-scale, compared with the other deep-learning based applications [65].…”

Section: Resultsmentioning

confidence: 99%

Important citations identification by exploiting generative model into discriminative model

Sun

et al. 2021

Journal of Information Science

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Although the citations between scientific documents are deemed as a vehicle for dissemination, inheritance and development of scientific knowledge, not all citations are well-positioned to be equal. A plethora of taxonomies and machine-learning models have been implemented to tackle the task of citation function and importance classification from qualitative aspect. Inspired by the success of kernel functions from resulting general models to promote the performance of the support vector machine (SVM) model, this work exploits the potential of combining generative and discriminative models for the task of citation importance classification. In more detail, generative features are generated from a topic model, citation influence model (CIM) and then fed to two discriminative traditional machine-learning models, SVM and RF (random forest), and a deep learning model, convolutional neural network (CNN), with other 13 traditional features to identify important citations. The extensive experiments are performed on two data sets with different characteristics. These three models perform better on the data set from one discipline. It is very possible that the patterns for important citations may vary by the fields, which disable machine-learning models to learn effectively the discriminative patterns from publications from multiple domains. The RF classifier outperforms the SVM classifier, which accords with many prior studies. However, the CNN model does not achieve the desired performance due to small-scaled data set. Furthermore, our CIM model–based features improve further the performance for identifying important citations.

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Section: Resultsmentioning

confidence: 99%

Important citations identification by exploiting generative model into discriminative model

Sun

et al. 2021

Journal of Information Science

Self Cite

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“…Despite our framework contribution in the field we still think that there is more work to be done for future researchers in weak signal detection literature, for example the nature of data to be analyzed in weak signal detection research is unstructured, thus the need for more advanced clustering methods to perform unsupervised machine learning to label data as weak signals from the past data, and apply Text mining Deep Learning models [43] in order to be able to extract and identify weak signals in future data once available online, which will give a competitive advantage for organizations. In our future work we will apply Graph embedding technique [44] [45] as a technology that will allow us to reduce the dimensionality of the corpus and facilitate the semantic representation of weak signals, through the study of dynamic graph embedding to monitor the evolution of a domain terminology through time, in hope of detecting innovation, opportunity or a threat as early as possible.…”

Section: Discussionmentioning

confidence: 99%

A Framework for Weak Signal Detection in Competitive Intelligence using Semantic Clustering Algorithms

Adil¹,

Abdelhadi²

2021

IJACSA

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Companies nowadays are sharing a lot of data on the web in structured and unstructured format, the data holds many signals from which we can analyze and detect innovation using weak signal detection approaches. To gain a competitive advantage over competitors, the velocity and volume of data available online must be exploited and processed to extract and monitor any type of strategic challenge or surprise whether it is in form of opportunities or threats. To capture early signs of a change in the environment in a big data context where data is voluminous and unstructured, we present in this paper a framework for weak signal detection relying on the crawling of a variety of web sources and big data based implementation of text mining techniques for the automatic detection and monitoring of weak signals using an aggregation approach of semantic clustering algorithms. The novelty of this paper resides in the capability of the framework to extend to an unlimited amount of unstructured data, that needs novel approaches to analyze, and the aggregation of semantic clustering algorithms for better automation and higher accuracy of weak signal detection. A corpus of scientific articles and patents is collected in order to validate the framework and provide a use case for future interested researchers in identifying weak signals in a corpus of data of a specific technological domain.

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“…More recent techniques, notably deep neural networks, seem to be a major avenue for improvement. Chen et al (2020) present a data mining approach based on patents. This approach is not dedicated to the extraction of parameters, but the tools used can be adapted for use in the TRIZ framework.…”

Section: Contradiction and Parameter Miningmentioning

confidence: 99%

PaTRIZ: A framework for mining TRIZ contradictions in patents

Guarino

Samet

Cavallucci

2022

Expert Systems with Applications

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A deep learning based method for extracting semantic information from patent documents

Cited by 54 publications

References 37 publications

Important citations identification by exploiting generative model into discriminative model

Important citations identification by exploiting generative model into discriminative model

A Framework for Weak Signal Detection in Competitive Intelligence using Semantic Clustering Algorithms

PaTRIZ: A framework for mining TRIZ contradictions in patents

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