Topic Embeddings - A New Approach to Classify Very Short Documents Based on Predefined Topics.
Lasse LommelMeike RieblingBurkhardt FunkChristian JungingerPublished in: Wirtschaftsinformatik (2019)
Keyphrases
- document set
- topic modeling
- latent topics
- topic models
- topic hierarchy
- topic specific
- topic discovery
- related topics
- latent dirichlet allocation
- topic extraction
- statistical topic models
- text documents
- topic detection
- document summaries
- topic hierarchies
- information retrieval
- topic drift
- document collections
- query topic
- keywords
- lda model
- related documents
- document classification
- news stories
- document content
- expert finding
- topic segmentation
- user interests
- text corpora
- document clustering
- vector space
- news items
- text analysis
- newspaper articles
- relevant documents
- text data
- text mining
- scientific papers
- web documents
- multi document summarization
- automatically created
- text streams
- author topic model
- topic detection and tracking
- blog posts
- hot topics
- text collections
- news articles
- probabilistic topic models
- dimensionality reduction
- news topics
- information retrieval systems
- textual content
- automatic classification
- co occurrence
- classify documents
- search engine
- probabilistic model
- information extraction
- text classification
- document representation
- trend analysis
- writing style
- concept space