8/18/2023 0 Comments Coherence x 3![]() The tcm should represent term co-occurrences within a boolean sliding window of size 110 (internally probabilities are used)īelow mentioned paper is the main theoretical basis for this code.Ĭurrently only a selection of metrics stated in this paper is included in this R implementation.Īuthors: Roeder, Michael Both, Andreas Hinneburg, Alexander (2015) (instead of the similarity between each pair). On this basis, the cosine similarity between each vector and the sum of all vectors is calculated The tcm should represent term co-occurrences within a boolean sliding window of size 5 (internally probabilities are used)įirst, a vector of npmi values for each top word is calculated as in "mean_npmi_cosim". On this basis, the cosine similarity between each pair of vectors is calculated. This result in a vector of npmi values for each top word. The tcm should represent the boolean term co-occurrence (internally probabilities are used)įirst, the npmi of an individual top word with each of the top words is calculated as in "mean_npmi". This metric may perform better than the simpler pmi metric. Similar (in terms of all parameter settings, etc.) to "mean_pmi" metricīut using the normalized pmi instead, which is calculated as This metric is similar to the UCI metric, however, with a smaller smoothing constant by default 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. 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 N_doc_tcm is used to calculate term probabilities from term counts as required for several metrics. The integer number of documents or text windows that was used to create the tcm. Numeric smoothing constant to avoid logarithm of zero. Please refer to the details section for more information on the metrics. ![]() Currently the following metrics are implemented:Ĭ("mean_logratio", "mean_pmi", "mean_npmi", "mean_difference", "mean_npmi_cosim", "mean_npmi_cosim2"). Is internally reduced to the top word space, i.e., all unique terms of x.Ĭharacter vector specifying the metrics to be calculated. Please also note that some efforts during any pre-processing steps might be skipped since the tcm With all entries in the lower triangle (excluding diagonal) set to zero (see, e.g., create_tcm). Please note that a memory efficient version of the tcm is assumed as input Serving as the reference to calculate coherence metrics. The term co-occurrence matrix, e.g, a Matrix::sparseMatrix or base::matrix, Terms of x have to be ranked per topic starting with rank 1 in row 1. A character matrix with the top terms per topic (each column represents one topic),
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