gensim

I tried doing text clustering using LDA, but it isn't giving me distinct clusters. Below is my code #Import libraries from gensim import corpora, models import pandas as pd from gensim.parsing.preprocessing import STOPWORDS from itertools import chain #stop words stoplist = list(STOPWORDS) new = ['education','certification','certificate','certified'] stoplist.extend(new) stoplist.sort() #read data dat = pd.read_csv('D:\data_800k.csv',encoding='latin').Certi.tolist() #remove stop words texts = [[word for word in document.lower().split() if word not in stoplist] for document in dat] #dictionary

I have a 60000 documents which i processed in gensim and got a 60000*300 matrix. I exported this as a csv file. When i import this in ELKI environment and run Kmeans clustering, i am getting below error. Task failed de.lmu.ifi.dbs.elki.data.type.NoSupportedDataTypeException: No data type found satisfying: NumberVector,field AND NumberVector,variable Available types: DBID DoubleVector,variable,mindim=266,maxdim=300 LabelList at de.lmu.ifi.dbs.elki.database.AbstractDatabase.getRelation(AbstractDatabase.java:126) at de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm.run(AbstractAlgorithm.java:81) at

I have an existing gensim Doc2Vec model, and I'm trying to do iterative updates to the training set, and by extension, the model. I take the new documents, and perform preproecssing as normal: stoplist = nltk.corpus.stopwords.words('english') train_corpus= [] for i, document in enumerate(corpus_update['body'].values.tolist()): train_corpus.append(gensim.models.doc2vec.TaggedDocument([word for word in gensim.utils.simple_preprocess(document) if word not in stoplist], [i])) I then load the original model, update the vocabulary, and retrain: #### Original model ## model = gensim.models.doc2vec

I am using doc2vec to convert the top 100 tweets of my followers in vector representation (say v1.....v100). After that I am using the vector representation to do the K-Means clusters. model = Doc2Vec(documents=t, size=100, alpha=.035, window=10, workers=4, min_count=2) I can see that cluster 0 is dominated by some values (say v10, v12, v23, ....). My question is what does these v10, v12 ... etc represents. Can I deduce that these specific column clusters specific keywords of document. Don't use the individual variables. They should be only analyzed together because of the way these embeddings

Just reading through the doc2vec commands on the gensim page. I am curious about the command"intersect_word2vec_format" . My understanding of this command is it lets me inject vector values from a pretrained word2vec model into my doc2vec model and then train my doc2vec model using the pretrained word2vec values rather than generating the word vector values from my document corpus. The result is that I get a more accurate doc2vec model because I am using pretrained w2v values which was generated from a much larger corpus of data compared to my relatively small document corpus. Is my

I am trying to calculate similarity. First of all i used RAKE library to extract the keywords from the crawled jobs. Then I put the keywords of every jobs into separate array and then combined all those arrays into documentArray. documentArray = ['Anger command,Assertiveness,Approachability,Adaptability,Authenticity,Aggressiveness,Analytical thinking,Molecular Biology,Molecular Biology,Molecular Biology,molecular biology,molecular biology,Master,English,Molecular Biology,,Islamabad,Islamabad District,Islamabad Capital Territory,Pakistan,,Rawalpindi,Rawalpindi,Punjab,Pakistan'"], ['competitive

I have a set of embeddings trained with a neural network that has nothing to do with gensim's word2vec. I want to use these embeddings as the initial weights in gensim.Word2vec . Now what I did see is that I can model.load(SOME_MODEL) and then continue training, but it requires a gensim modle as input. Also reset_from() seems to only accept other gensim model. But in my case, I don't have a gensim model to start from, but a text file in word2vec format of embeddings. So how do I start transfer learning from an word2vec text file to gensim.Word2vec ? You can load other models using the key

I have a large pretrained Word2Vec model in gensim from which I want to use the pretrained word vectors for an embedding layer in my Keras model. The problem is that the embedding size is enormous and I don't need most of the word vectors (because I know which words can occure as Input). So I want to get rid of them to reduce the size of my embedding layer. Is there a way to just keep desired wordvectors (including the coresponding indices!), based on a whitelist of words? Thanks to this answer (I've changed the code a little bit to make it better). you can use this code for solving your