I'm going crazy in this project. This is multi-label text-classification with lstm in keras. My model is this: model = Sequential() model.add(Embedding(max_features, embeddings_dim, input_length...

I am referring (here) to freeze models into .pb file. My model is CNN for text classification I am using (Github) link to train CNN for text classification and exporting in form of models. I have t...

I have check the previous post link but it doesn't seems to work for my case:- I have pre trained word2vec model: import gensim model = Word2Vec.load('w2v_model') Now I have a pandas dataframe...

I am getting the following error while calling the model.predict function when running a text classification model in keras. I searched the everywhere but it isn't working for me. ValueError: Erro...

I am using scikit-learn for classification of text documents(22000) to 100 classes. I use scikit-learn's confusion matrix method for computing the confusion matrix. model1 = LogisticRegressi...

I am using bag of words to classify text. It's working well but I am wondering how to add a feature which is not a word. Here is my sample code. import numpy as np from sklearn.pipeline import P...

I have a csv, struct is CAT1,CAT2,TITLE,URL,CONTENT, CAT1, CAT2, TITLE ,CONTENT are in chinese. I want train LinearSVC or MultinomialNB with X(TITLE) and feature(CAT1,CAT2), both get this error. b...

I am familiar with using BOW features for text classification, wherein we first find the size of the vocabulary for the corpus which becomes the size of our feature vector. For each sentence/docume...

I am working on a text classification problem, that is, given some text, I need to assign to it certain given labels. I have tried using fast-text library by Facebook, which has two utilities of i...

I am doing multilabel classification, where I try to predict correct labels for each document and here is my code: mlb = MultiLabelBinarizer() X = dataframe['body'].values y = mlb.fit_transform(d...

Objective : Identifying class label using user entered question (like Question Answer system). Data extracted from Big PDF file, and need to predict page number based on user input. Majorly used...

I followed the tutorial here: (https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html) However, I modified the code to be able to save the generated model through h5py. Thus...

I am doing multi-label classification where I am trying to predict correct tags to questions: (X = questions, y = list of tags for each question from X). I am wondering, which decision_function_s...

I have been blowing my brains out over the past 2-3 weeks on this problem. I have a multi-label (not multi-class) problem where each sample can belong to several of the labels. I have around 4.5 m...

I try to do text classification naive bayes weka libarary in my java code, but i think the result of the classification is not correct, i don't know what's the problem. I use arff file for the inpu...

Please help me in understanding the difference between how TaggedDocument and LabeledSentence of gensim works. My ultimate goal is Text Classification using Doc2Vec model and any classifier. I am f...

Is there a good McNemar's test implemented in Python? I don't see it anywhere in Scipy.stats or Scikit-Learn. I may have overlooked some other good packages. Please recommend. McNemar's Test...

I'm trying to use FeatureUnion to extract different features from a datastructure, but it fails due to different dimensions: ValueError: blocks[0,:] has incompatible row dimensions Implementaio...

I am researching about embedding input for Convolution Neural Network and I understand Word2vec. However, in CNN text classification. dennybritz used function learn.preprocessing.VocabularyProcesso...

The text data is organized as vector with 20,000 elements, like [2, 1, 0, 0, 5, ...., 0]. i-th element indicates the frequency of the i-th word in a text. The ground truth label data is also rep...

I have a question regarding the defining of vocabulary set needed for feature extraction in text classification. In an experiment, there are two approaches I can think of: 1.Define vocabulary siz...

I am using SVM to classify my text where in i don't actually get the result instead get with numerical probabilities. Dataframe (1:20 trained set, 21:50 test set) Updated: ou <- structure(li...

Is there any example on how can TensorFlow be used for text classification using neural networks?

I've successfully followed this example for my own text classification script. The problem is I'm not looking to process pieces of a huge, but existing data set in a loop of partial_fit calls, lik...

I am trying to build a NaiveBayes classifier, loading the data from database as DataFrame which contains (label, text). Here's the sample of data (multinomial label): label| feature| +-----+------...