Topic Subject Creation Using Unsupervised Learning for Topic Modeling
نویسندگان
چکیده
منابع مشابه
Topic Modeling and Classification of Cyberspace Papers Using Text Mining
The global cyberspace networks provide individuals with platforms to can interact, exchange ideas, share information, provide social support, conduct business, create artistic media, play games, engage in political discussions, and many more. The term cyberspace has become a conventional means to describe anything associated with the Internet and the diverse Internet culture. In fact, cyberspac...
متن کاملLearning subject drift for topic tracking
For topic tracking where data is collected over an extended period of time, the discussion of a topic, i.e. the in a story changes over time. This paper focuses on subject drift and presents a method for topic tracking on broadcast news stories to handle subject drift. The basic idea is to automatically extract the optimal positive training data of the target topic so as to include only the dat...
متن کاملTopic tracking using subject templates
Topic tracking, which starts from a few sample stories and finds all subsequent stories that discuss the same topic, is a new challenge for the text categorization task and makes a significant contribution to the accessibility of information, such as archives of news, e-mails, and historical newspapers. Much previous research on topic tracking uses machine learning techniques. However, the smal...
متن کاملText Modeling using Unsupervised Topic Models and Concept Hierarchies
Statistical topic models provide a general data-driven framework for automated discovery of highlevel knowledge from large collections of text documents. While topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, on the other hand, tend to be semantically richer due to careful selecti...
متن کاملBayesian Unsupervised Topic Segmentation
This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of wellformed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer and Information Science
سال: 2020
ISSN: 1913-8997,1913-8989
DOI: 10.5539/cis.v13n3p57