Abstract
A reference library can be described as a corpus of an individual composition of documents. Over time, the corpus might grow because an agent decides to extend its corpus with additional documents, e.g., new publications, or new articles. Existing approaches use topic modelling techniques to compare documents with each other within the same corpus by the documents' topic distribution. However, for new documents, only the text, and no topic distribution is available. Thus, this paper describes three techniques for estimating topic distributions of new unseen documents considering the initial documents in a corpus. Additionally, we present an extensive evaluation about the performance and runtime of the three topic modelling techniques for various scenarios and different sized corpora.
| Original language | English |
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| Title of host publication | 2020 IEEE 14th International Conference on Semantic Computing (ICSC) |
| Number of pages | 8 |
| Publisher | IEEE |
| Publication date | 02.2020 |
| Pages | 451-458 |
| Article number | 9031467 |
| ISBN (Print) | 978-1-7281-6333-8 |
| ISBN (Electronic) | 978-1-7281-6332-1 |
| DOIs | |
| Publication status | Published - 02.2020 |
| Event | 14th IEEE International Conference on Semantic Computing - San Diego, United States Duration: 03.02.2020 → 05.02.2020 Conference number: 158497 |
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
- Research Area: Intelligent Systems
DFG Research Classification Scheme
- 409-06 Information Systems, Process and Knowledge Management
