Topic models extract representative word sets - called topics - from word counts in documents without requiring any semantic annotations. Topics are not guaranteed to be well interpretable, therefore, coherence measures have been proposed to distinguish between good and bad topics.
Mar 25, 2014
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Coherence measures have been proposed in the NLP community to evaluate topics constructed by some topic model. In a more general setting, coherence measures ...
Coherence measures or coherence analysis are employed to distinguish between good and bad topics based on the main n words regarding their interpretability. ...
Feb 19, 2019 · Coherence measures the relative distance between words within a topic. There are two major types C_V typically 0 < x < 1 and uMass -14 < x ...
Jan 10, 2022 · We explored the blocks that compose a Topic Coherence Measure: Segmentation, Probability Calculation, Confirmation Measure, and Aggregation, ...
This work treats a wordy topic as an object and quantitatively describe it via normalized mean values of pair-wise word similarities to perform topic ...
This paper introduces distributional semantic similarity methods for automatically measuring the coherence of a set of words generated by a topic model. We ...
Jun 13, 2019 · I read that the coherence measure is better because it is correlated with human topic interpretation. ... topic model evaluation metrics.
Topic coherence measures have been proposed that quantify the semantic coherence of topic words by analyzing their co-occurrences in a larger reference corpus.
Nov 2, 2018 · The coherence score is for assessing the quality of the learned topics. For one topic, the words i,j being scored in ∑i<jScore(wi,wj) have ...