I have a tf-idf matrix already, with rows for terms and columns for documents. Now I want to train a LDA model with the given terms-documents matrix. The first step seems to be using gensim.matutils.Dense2Corpus to convert the matrix into the corpus format. But how to construct the id2word parameter? I have the list of the terms (#terms==#rows) but I don't know the format of the dictionary so I cannot construct the dictionary from functions like gensim.corpora.Dictionary.load_from_text. Any suggestions? Thank you.
Training a LDA model with gensim from some external tf-idf matrix and term list
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id2wordmust map each id (integer) to term (string).In other words, it must support
id2word[123] == 'koala'.A plain Python
dictis the easiest option.