Latent keyphrase generation by combining contextually similar primitive words

Cho, Taemin; Cho, Hanna; Lee, Jae-Dong; Lee, Jee-Hyong

doi:10.1109/scis-isis.2014.7044871

Cited by 1 publication

(2 citation statements)

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“…Their algorithm conjugated single word embeddings as an external knowledge to select semantically similar word embeddings with the document embedding as abstractive keyphrases. Cho et al [9] extracted primitive words that are important single words in the document and combined the two contextually similar primitive words as latent keyphrases. However, both methods had a limitation on length.…”

Section: Related Workmentioning

confidence: 99%

“…However, only a small portion of documents contains author-assigned keyphrases and a majority of documents do not have keyphrases. Therefore, extracting keyphrases from documents has become one of the main concerns in recent days, and there have been several studies on automatic keyphrase extraction task [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Latent Keyphrase Extraction Using Deep Belief Networks

Lee

2015

Int. J. Fuzzy Log. Intell. Syst.

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Nowadays, automatic keyphrase extraction is considered to be an important task. Most of the previous studies focused only on selecting keyphrases within the body of input documents. These studies overlooked latent keyphrases that did not appear in documents. In addition, a small number of studies on latent keyphrase extraction methods had some structural limitations. Although latent keyphrases do not appear in documents, they can still undertake an important role in text mining because they link meaningful concepts or contents of documents and can be utilized in short articles such as social network service, which rarely have explicit keyphrases. In this paper, we propose a new approach that selects qualified latent keyphrases from input documents and overcomes some structural limitations by using deep belief networks in a supervised manner. The main idea of this approach is to capture the intrinsic representations of documents and extract eligible latent keyphrases by using them. Our experimental results showed that latent keyphrases were successfully extracted using our proposed method.

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Section: Related Workmentioning

confidence: 99%