Cocitation and co-word methods have long been used to detect and track emerging topics in scientific literature, but both have weaknesses. Recently, while many researchers have adopted generative probabilistic models for topic detection and tracking, few have compared generative probabilistic models with traditional cocitation and co-word methods in terms of their overall performance. In this a...

This paper proposed to solve the problems of incomplete coverage and low accuracy in keyword extraction of Chinese text based on intrinsic feature of the Chinese language and an extraction method of multidimensional information weighted eigenvalues. This method combined theoretical analysis and experimental calculation to study the parts of speech, word position, word length, semantic similarit...

1- Assistant Prof. of LIS, Department Information Management, Regional Center for Science & Technology (RICeST), Shiraz, Iran , [email protected] 2- Graduated knowledge and Science, Shemiranat Public Libraries Office, Tehran, 3- Ph.D. in School Psychology Educational Sciences, Kharazmi University,

In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset spontaneous speech. We examine measures structure these for significant differences, finding that have a lower heterogeneity centralization, but higher edge density. then use measures, network embedding method some frequency d...

Representing a word by its co-occurrences with other words in context is an effective way to capture the meaning of the word. However, the theory behind remains a challenge. In this work, taking the example of a word classification task, we give a theoretical analysis of the approaches that represent a word X by a function f(P(C|X)), where C is a context feature, P(C|X) is the conditional proba...