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For quick proof of concept you could take pretrained embedder i.e. LASER. Here is unofficial pypi package. It works just fine. Though, keep in mind, embedders are meant for rather shorter chunks of texts. It makes little sense to assign single semantic meaning to more than few sentences. Embedder produces numerical vector. Once you've embedded lyrics from ...


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You can use the scipy module to calculate similarities. For your example, this would work like this: import pandas as pd users = ["U1", "U2", "U3", "U4", "U5", "U6"] X = pd.DataFrame({ "Dep1": [0, 1, 1, 0, 0, 1], "Dep2": [1, 0, 0, 1, 1, 0], "Comp1": [0, 0, 1, 0, 0, 1], "Comp2": [1, 1, 0, 1, 1, 0], "Site1": [0, 1, 1, 0, 1, 0], "Site2": [1, 0, 0, 1, 0, 1], "...


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If I understand correctly, you're trying to map abstracts to their research papers. Here is a simple starting point: Compute a TF IDF model using the entire corpus (all abstracts + research papers). Use this model to transform your abstracts and research papers into a weighted vector representation. Under the TF IDF weighting scheme, these documents will ...


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Try google universal sentence encoder ,check out the colab version of UC and just replace the example query in there with your 2 query it will give the score of the similarity between any two sentence . its the best to calculate semantic text similarity


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You could take pretrained embedder and look for distances between the embeddings. There's LASER from Facebook. This is an unofficial pypi package, it replaces some of the internal tools used for tokenization and BPE encodings. I've used it extensively and it works just fine. It encodes your text as 1024-element numerical vector. Then you can just calculate ...


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Levenshtein distance (and its cousing Jaro, Hemming etc...) Levenshtein distance for measuring the difference between two sequences between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word (your case one set of characters) into the other. There are a couple implementations, for ...


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Outlier detection doesn't sound like the most promising approach to me, as you have a model for the data. Some ideas you could try: use a hypothesis test to check the hypothesis that the stress values fit iid Gaussian with pre-defined standard deviation and unknown mean; use linear regression to fit a line that predicts stress as a function of time, and see ...


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