gensim

问题 word2vec is a open source tool by Google: For each word it provides a vector of float values, what exactly do they represent? There is also a paper on paragraph vector can anyone explain how they are using word2vec in order to obtain fixed length vector for a paragraph. 回答1: TLDR : Word2Vec is building word projections ( embeddings ) in a latent space of N dimensions, (N being the size of the word vectors obtained). The float values represents the coordinates of the words in this N

问题 After reading the tutorial at gensim's docs, I do not understand what is the correct way of generating new embeddings from a trained model. So far I have trained gensim's fast text embeddings like this: from gensim.models.fasttext import FastText as FT_gensim model_gensim = FT_gensim(size=100) # build the vocabulary model_gensim.build_vocab(corpus_file=corpus_file) # train the model model_gensim.train( corpus_file=corpus_file, epochs=model_gensim.epochs, total_examples=model_gensim.corpus

问题 After reading the tutorial at gensim's docs, I do not understand what is the correct way of generating new embeddings from a trained model. So far I have trained gensim's fast text embeddings like this: from gensim.models.fasttext import FastText as FT_gensim model_gensim = FT_gensim(size=100) # build the vocabulary model_gensim.build_vocab(corpus_file=corpus_file) # train the model model_gensim.train( corpus_file=corpus_file, epochs=model_gensim.epochs, total_examples=model_gensim.corpus

问题 As we know machines can't understand the text but it understands numbers so in NLP we convert text to some numeric representation and one of them is BOW representation. Here, my objective is to convert every document to some numeric representation and save it for future use. And I am following the below way to do that by converting text to BOW and saving it in a pickle file. My question is, whether we can do this in a better and reliable way? so that every document can be saved as some vector

问题 I'm quite new to Python and coding in general, so I seem to have run into an issue. I'm trying to run this code (credit to Matthew Mayo, whole thing can be found here): # import warnings # warnings.filterwarnings(action = 'ignore', category = UserWarning, module = 'gensim') import sys from gensim.corpora import WikiCorpus def make_corpus (in_f, out_f): print(0) output = open(out_f, 'w', encoding = 'utf-8') print(1) wiki = WikiCorpus(in_f) print(2) i = 0 for text in wiki.get_texts(): output

问题 I have trained fasttext model with Gensim over the corpus of very short sentences (up to 10 words). I know that my test set includes words that are not in my train corpus, i.e some of the words in my corpus are like "Oxytocin" "Lexitocin", "Ematrophin",'Betaxitocin" given a new word in the test set, fasttext knows pretty well to generate a vector with high cosine-similarity to the other similar words in the train set by using the characters level n-gram How do i incorporate the fasttext model

问题 I have trained fasttext model with Gensim over the corpus of very short sentences (up to 10 words). I know that my test set includes words that are not in my train corpus, i.e some of the words in my corpus are like "Oxytocin" "Lexitocin", "Ematrophin",'Betaxitocin" given a new word in the test set, fasttext knows pretty well to generate a vector with high cosine-similarity to the other similar words in the train set by using the characters level n-gram How do i incorporate the fasttext model

问题 I have trained a doc2vec and corresponding word2vec on my own corpus using gensim. I want to visualise the word2vec using t-sne with the words. As in, each dot in the figure has the "word" also with it. I looked at a similar question here : t-sne on word2vec Following it, I have this code : import gensim import gensim.models as g from sklearn.manifold import TSNE import re import matplotlib.pyplot as plt modelPath="/Users/tarun/Desktop/PE/doc2vec/model3_100_newCorpus60_1min_6window

问题 I have trained a doc2vec and corresponding word2vec on my own corpus using gensim. I want to visualise the word2vec using t-sne with the words. As in, each dot in the figure has the "word" also with it. I looked at a similar question here : t-sne on word2vec Following it, I have this code : import gensim import gensim.models as g from sklearn.manifold import TSNE import re import matplotlib.pyplot as plt modelPath="/Users/tarun/Desktop/PE/doc2vec/model3_100_newCorpus60_1min_6window

问题 I am facing the following error when trying to update my gensim's LdaModel: IndexError: index 6614 is out of bounds for axis 1 with size 6614 I checked why were other people having this issue on this thread, but I am using the same dictionary from the beginning to the end, which was their error. As I have a big dataset, I am loading it chunk by chunk (using pickle.load). I am building the dictionary in this way, iteratively, thanks to this piece of code : fr_documents_lda = open("documents