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In theory it's of course possible to reach perfect performance: if the algorithm can find what it needs in the features to correctly distinguish between classes (or clusters), then it will perform perfectly. In reality however it's very rare that performance is perfect, because: Text data is noisy and extremely diverse Most of the time when there is a way ...


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Word2Vec algorithms (Skip Gram and CBOW) treat each word equally, because their goal to compute word embeddings. The distinction becomes important when one needs to work with sentences or document embeddings; not all words equally represent the meaning of a particular sentence. And here different weighting strategies are applied, TF-IDF is one of those ...


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Paraphrase detection is still a very active and very challenging research area, so it's unlikely that there are full-fledged standard libraries for this task since there is still no clear "best solution" to this problem. In order to build a corpus you might want to look at how shared tasks/competitions have done it before. I know at least of SemEval which ...


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Using NER (more generally sequence labeling) means classifying every token in the sentence, so if the goal is only to label every sentence there's no strong need for it in your case. However NER might be more appropriate in case the order of the words is important, because sequence labeling models take it into account whereas traditional text classification ...


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I would like to make the argument that you actually cannot have statistically speaking 100.00% accuracy even in theory but you can get really close. However, you getting too close might mean that your overfitting. This is because you cannot have statistically speaking absolute zero uncertainty in any system of more than 2 predictors that are independent or ...


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