Title :
Latent keyphrase generation by combining contextually similar primitive words
Author :
Taemin Cho ; Hana Cho ; Jaedong Lee ; Jee-Hyong Lee
Author_Institution :
Dept. of Electr. & Comput. Eng., Sungkyunkwan Univ., Suwon, South Korea
Abstract :
As the number of document resources is continuously increasing, automatically extracting keyphrases from a document becomes one of the main issues in recent days. However, most previous work overlook keyphrases which is nonexistent in the document. Although latent keyphrases do not appear in the document, it can be important because it represents the meaningful concepts or keypoints of the document. We have discovered that the portion of latent keyphrases is more than one fourth of the entire keyphrases. Latent keyphrases also take an important role as much as existential keyphrases in documents. In this paper, we propose an approach to find latent keyphrases of a document in a given document set. The main idea of this approach is to generate keyphrase by choosing primitive words and combining them considering their context in documents. Experiment result shows that latent keyphrase can be extracted by our approach.
Keywords :
feature extraction; feature selection; word processing; document resource; latent keyphrase extraction; latent keyphrase generation; primitive word selection; Computational linguistics; Context; Equations; Feature extraction; Information retrieval; Mathematical model; Semantics; contextual similiarity; keyphrase generation; neighbor document; primitive word combination;
Conference_Titel :
Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on
DOI :
10.1109/SCIS-ISIS.2014.7044871