Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation

By Shaheen Syed, Marco René Spruit. International Conference on Data Science and Advanced Analytics, 2017

363 citations recorded by the source corpus.

Subfields: Artificial Intelligence, General Social Sciences

View Paper: https://www.semanticscholar.org/paper/7e499ef94e4d9a46eef2f09097ef1c6e285a18b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dan Jurafsky
2. D. Angelov
3. Mohit Bansal
4. Noah A. Smith
5. Kyunghyun Cho
6. Christopher D. Manning
7. Yonatan Belinkov
8. Luke S. Zettlemoyer

In General Social Sciences

1. Dan Jurafsky
2. D. Angelov

Citations in the corpus

• Top2Vec: Distributed Representations of Topics (2020, background): “As such, the highest probability words in a topic are usually words such as the , and , it and other common words in the language [4].”
• The Diversity–Innovation Paradox in Science (2019, methodology): “t-exclusive within a topic. This helps identify concepts that are both common and distinctive to balance generality and exclusivity. To get at this, we extract the top terms based on their FREX-score (24). FREX-scores compound the weighted frequency and exclusivity of a term in a topic. Here we expl”