This paper introduces distributional semantic similarity methods for automatically measuring the coherence of a set of words generated by a topic model. We construct a semantic space to represent each topic word by making use of Wikipedia as a reference corpus to identify context features and collect frequencies. Relatedness between topic words and context features is measured using variants of Pointwise Mutual Information (PMI). Topic coherence is determined by measuring the distance between these vectors computed using a variety of metrics. Evaluation on three data sets shows that the distributional-based measures outperform the state-of-the-art approach for this task.
REF introduced distributional semantic similarity methods for computing coherence, calculating the distributional similarity between semantic vectors for the top-N topic words using a range of distributional similarity measures such as cosine similarity and the Dice coefficient.
Evaluating Topic Coherence Using Distributional Semantics
IWCS
Computer Science