![]() The parts are grouped into dimensions that span the configuration space of coherence measures. Aggregation: It’s the central lab where you combine all the quality numbers and derive a single number for overall quality.įrom a technical point of view, Coherence framework is represented as a composition of parts that can be combined.For example, 75% of products are good quality as per XXX standard. Confirmation Measure: Determine quality as per some predefined standard (say % conformance) and assign some number to qualify.Probability Estimation: Quantitative Measurement of sub lot quality.Segmentation: A lot of dispatch product divided into different sub-lot sizes, such that each sub-lot product are different.The quality lab setup is the topic coherence framework, which is grouped into 4 following dimensions: The qualitative approach is to test the topics on their human interpretability by presenting them to humans and taking their input on them. The dispatch product here is the topics from some topic modeling algorithm such as LDA. You don’t need to rely on people reviews, as you have a good quantitative measure of quality. Now, while sitting at the central lab, you can get the quality values from 4 Kiosks and can compute your overall quality. To arrive at the quantitative measure, your central lab at X set up 4 different quality lab Kiosk at A, B, C and D to check the dispatch product quality (let’s say quality defined by % of conformance as per some predefined standards). So, basically, you are evaluating on the qualitative approach, as there is no quantitative measure involved, which can tell you how much worse your dispatch product quality at A is compared to dispatch quality at B. You may need to improve your process if most people give you bad reviews. One way is to collect the reviews from various people – for example- “whether they receive product in good condition”, Did they receive on time”. Imagine you are a lead quality analyst sitting at location X at a logistics company and you want to check the quality of your dispatch product at 4 different locations: A, B, C, D. Let’s start learning with a simple example and then we move to a technical part of topic coherence. ![]() This is an attractive method to bring structure to otherwise unstructured text data, but Topics are not guaranteed to be well interpretable, therefore, coherence measures have been proposed to distinguish between good and bad topics. Topic models learn topics-typically represented as sets of important words-automatically from unlabelled documents in an unsupervised way. Latent Dirichlet Allocation (LDA) is a widely used topic modeling technique to extract topic from the textual data. There are many techniques that are used to obtain topic models. Topic modeling provides us with methods to organize, understand and summarize large collections of textual information. In this article, we will go through the evaluation of Topic Modelling by introducing the concept of Topic coherence, as topic models give no guaranty on the interpretability of their output. ![]()
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