In an external reference corpus and, therefore, is an extrinsic metric in the standard use case. The tcm should represent term co-occurrences within a boolean sliding window of size 10 (internally probabilities are used) That subsets the lower or upper triangle of tcm, e.g. Where x and y are term index pairs from an arbitrary term index combination The pointwise mutual information is calculated as This metric is similar to the UMass metric, however, with a smaller smoothing constant by defaultĪnd using the mean for aggregation instead of the sum. In the original documents and, therefore, is an intrinsic metric in the standard use case. The tcm should represent the boolean term co-occurrence (internally the actual counts are used) Where x and y are term index pairs from a "preceding" term index combination. That logratio, pmi and nmpi usually opt for smaller numbers, whereas the other metrics rather tend to propose higher numbers. From initial experience it may be assumed Might be considered for direct comparison.Įach metric usually opts for a different optimum number of topics. Note that for all currently implemented metrics the tcm is reduced to the top word space on basis of the terms in x.Ĭonsidering the use case of finding the optimum number of topics among several models with different metrics,Ĭalculating the mean score over all topics and normalizing this mean coherence scores from different metrics Note that depending on the use case, still, different settings than the standard settings for creation of tcm may be reasonable. That served for definition of standard settings for individual metrics.
#Umass coherence score how to#
The currently implemented coherence metrics are described below including a description of theĬontent type of the tcm that showed good performance in combination with a specific metric.įor details on how to create tcm see the example section.įor details on performance of metrics see the resources in the reference section ValueĪ numeric matrix with the coherence scores of the specified metrics per topic. N_doc_tcm is used to calculate term probabilities from term counts as required for several metrics.
#Umass coherence score windows#
The integer number of documents or text windows that was used to create the tcm.
0 kommentar(er)
