Latent Keyphrase Extraction Using Deep Belief Networks

Jo, Taemin; Lee, Jee-Hyong

doi:10.5391/ijfis.2015.15.3.153

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…They build a KG system based on loosely structured ontologies. Authors of [39] rely on Deep Belief Networks described in [40] to capture the intrinsic representations of documents and using them to extract keyphrases.…”

Section: Other Methodsmentioning

confidence: 99%

Keyphrase Generation: A Multi-Aspect Survey

Çano

Bojar

2019

2019 25th Conference of Open Innovations Association (FRUCT)

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Extractive keyphrase generation research has been around since the nineties, but the more advanced abstractive approach based on the encoder-decoder framework and sequenceto-sequence learning has been explored only recently. In fact, more than a dozen of abstractive methods have been proposed in the last three years, producing meaningful keyphrases and achieving state-of-the-art scores. In this survey, we examine various aspects of the extractive keyphrase generation methods and focus mostly on the more recent abstractive methods that are based on neural networks. We pay particular attention to the mechanisms that have driven the perfection of the later. A huge collection of scientific article metadata and the corresponding keyphrases is created and released for the research community. We also present various keyphrase generation and text summarization research patterns and trends of the last two decades.

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Section: Other Methodsmentioning

confidence: 99%

Keyphrase Generation: A Multi-Aspect Survey

Çano

Bojar

2019

2019 25th Conference of Open Innovations Association (FRUCT)

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“…In comparison to other existing methods, the algorithm MAUI, which combines Browns clustering and Word Vectors, holds a higher value for precision and recall [37]. Backpropagation neural networks have been suggested as a further technique for keyword extraction [40]. Journal articles made up of the corpora that were utilized to train the algorithm.…”

Section: Keyword Extractionmentioning

confidence: 99%

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

Sapana Kolambe

2023

IJRITCC

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In the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches.

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“… Jo & Lee (2015) use a Deep Belief Network (DBN) that connects to a logistic regression layer to learn a classifier. The model does not require any manually selected features.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact analysis of keyword extraction using contextual word embedding

Khan

Shahid

Uddin

et al. 2022

PeerJ Computer Science

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A document’s keywords provide high-level descriptions of the content that summarize the document’s central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document’s content that can reflect the authors’ main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors’ provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

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Latent Keyphrase Extraction Using Deep Belief Networks

Cited by 9 publications

References 12 publications

Keyphrase Generation: A Multi-Aspect Survey

Keyphrase Generation: A Multi-Aspect Survey

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

Impact analysis of keyword extraction using contextual word embedding

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