In topic modeling, topic coherence measures the quality of the data by comparing the semantic similarity between highly repetitive words in a topic [10]. Coherence score is a scale from 0 to 1 in which a good coherence (high similarity) has a score of 1, and a bad coherence (low similarity) has a score of 0 [11].
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Jan 10, 2022 · A topic coherence metric assesses is how well a topic is 'supported' by a text set (called reference corpus).
Topics are not guaran- teed to be well interpretable, therefore, coherence measures have been proposed to distinguish between good and bad topics.
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 ...
Mar 18, 2024 · We can use the coherence score in topic modeling to measure how interpretable the topics are to humans. In this case, topics are represented as ...
Jan 12, 2023 · In cᵥ coherence, each topic word is compared with the set of all topics. A boolean sliding window of size 110 is used to assess whether two ...
Topic coherence is determined by measuring the distance between these vectors computed using a variety of metrics.
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 ...
topic coherence with umass decrease when number of topics increase. Topic coherence with c_v goes up when number of topic increases. I guess the 'c_v' is the ...
Dec 6, 2019 · If you are looking for interpretability of your model, then topic coherence is the metric you want to optimize for. It is a metric that ...