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You could use clustering with a more basic similarity measure, for example cosine or even simply the proportion of words in common (e.g. Jaccard, overlap coefficient). This should gives you groups of sentences which are "quite similar" against each other, whereas sentences in different clusters are supposed to be very different. This way you would ...


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This is essentially information retrieval: usually there is a collection of documents and the goal is to find the document which is the most similar to a given query (what you call the "semantic concept"). The traditional way to do that is to convert the collection of documents as vectors, typically with TFIDF weight but there are many options (I ...


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