Multi-Dimension Topic Mining Based on Hierarchical Semantic Graph Model

Topic Mining Based on Graph Local Clustering

This paper introduces an approach for discovering thematically related document groups (a topic mining task) in massive document collections with the aid of graph local clustering. This can be achieved by viewing a document collection as a directed graph where vertices represent documents and arcs represent connections among these (e.g. hyperlinks). Because a document is likely to have more con...

Multi Domain Semantic Information Retrieval Based on Topic Model

Over the last decades, there have been remarkable shifts in the area of Information Retrieval (IR) as huge amount of information is increasingly accumulated on the Web. The gigantic information explosion increases the need for discovering new tools that retrieve meaningful knowledge from various complex information sources. Thus, techniques primarily used to search and extract important informa...

Semantic Emotion-Topic Model Based Social Emotion Mining

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

Building a Semantic Graph based on Sequential Language Model for Topic-Sensitive Content Extraction

Graph-based models have been explored to extract information of interest from a text collection. They can potentially incorporate related information to rank important contents. In this paper, we design a semantic graph model for topic-sensitive contents extraction. The topic-sensitive contents refer to segments of a document with respect to a certain aspect of a topic. For example, in online p...