Supervised Paragraph Vector: Distributed Representations of Words, Documents and Class Labels

Park, Eunjeong L.; Cho, Sungzoon; Kang, Pilsung

doi:10.1109/access.2019.2901933

Cited by 15 publications

(3 citation statements)

References 26 publications

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“…B) Feature extraction: NLP model cannot work on the raw text data directly. So, feature extraction methods are required to convert text into a numerical representation of features or a matrix (or vector) of features [82], [98], [102]. It maps words into numerical vector space, which is considered a richer representation of text input in NLP.…”

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3.The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.

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Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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“…To overcome these flaws, future research and work is necessary for this method. Park et al [26] proposed an algorithm that results the top search results for IR queries. For their approach Boolean interface is used without ranking functions.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

Devi¹,

Kumar²

2020

IJACSA

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As the size of the document collections are increasing day-by-day, finding an essential document clusters for classification problem is one of the major problem due to high inter and intra document variations. Also, most of the conventional classification models such as SVM, neural network and Bayesian models have high true negative rate and error rate for document classification process. In order to improve the computational efficacy of the traditional document classification models, a hybrid feature extraction-based document cluster approach and classification approaches are developed on the large document sets. In the proposed work, a hybrid glove feature selection model is proposed to improve the contextual similarity of the keywords in the large document corpus. In this work, a hybrid document clustering similarity index is optimized to find the essential key document clusters based on the contextual keywords. Finally, a hybrid document classification model is used to classify the clustered documents on large corpus. Experimental results are conducted on different datasets, it is noted that the proposed document clustering-based classification model has high true positive rate, accuracy and low error rate than the conventional models.

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“…The doc2vec model was proposed by Le et al (Le and Mikolov, 2014) (paragraph vectors) as a document-embedding method in 2014. This model is applicable in text classification and document similarity calculation (Park et al , 2019). Text classification is known as the task of classifying a given text into a set of predefined classes (Dalal and Zaveri, 2011).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence methods for improving the inventive design process, application in lattice structure case study

Hanifi

Chibane

Houssin

et al. 2022

AIEDAM

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Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of a complete map to clarify a problem situation. Therefore, the Inverse Problem Graph (IPG) method has been introduced within the IDM framework to enhance its agility. Nevertheless, the manual gathering of essential information, including parameters and concepts, requires effort and time. This paper integrates the neural network doc2vec and machine learning algorithms as Artificial Intelligence methods into a graphical method inspired by the IPG process. This integration can facilitate and accelerate the development of inventive solutions by extracting parameters and concepts in the inventive design process. The method has been applied to develop a new lattice structure solution in the material field.

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Supervised Paragraph Vector: Distributed Representations of Words, Documents and Class Labels

Cited by 15 publications

References 26 publications

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

Artificial intelligence methods for improving the inventive design process, application in lattice structure case study

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