Convolutional Neural Networks for Sentence Classification

When the code is used with a different dataset than the provided one it crashed if the longest sentence in the other dataset is longer than the longest sentence in the provided dataset. This can be fixed easily, the max length it set in the code manually.

Hi, I'm facing this error while trying to reproduce the experiments. Does anyone know how to solve this ? I have changed nothing and I completed the requirements. Thanks for your help :)

Hello, I'm sorry to write this, when I import this project into my workspace,there are two red line under two place,LeNetConvPoolLayer and MLPDropout,line 88 and 95 in the conv_net_classes.py file;then I add theano. prefix ,the line dispeared;but when I run the conv_net_classes.py file,it is wrong --AttributeError: 'module' object has no attribute 'LeNetConvPoolLayer',how can I dispose it?

Hi, I am trying to use CNN_sentence to classify tweets into one of the K predefined topics. Although I am able to get the final ouput class for each tweet, I am more interested in the probability vector in the layer right before the ouput layer based on which its decided which class the tweet should belong to. eg. If I have 3 classes and the output class for a given tweet is [2], the I assume the previous layer would be dealing with a class-wise probability which could be something like [0.3 , 0.78, 0.1] for class 1, 2, 3 respectively(just an example).

[test_loss,y_pred] = test_model_all(test_set_x,test_set_y) the variable "y_pred" gives me the final output but not the class probabilities but not class probabilities. Can you suggest a way to get these probs ?

Hi, @yoonkim I am a beginner of natural language processing and machine learning. Since 'GoogleNews-vectors-negative300.bin' file size is quite large, all of my attemps for making a pickle file ('mr.p') failed. Could you give me some pieces of advice for making 'mr.p' with 16GB~32GB RAM if you don't mind?

And.. I wonder if 'mr.p' also need a chunk process to solve the memory problem. (I little know about pickle file..)

Thank you

Hi everyone,

I try to run the "process_data.py" file with the same word2vec binary file (i.e. GoogleNews-vectors-negative300.bin)  but it didn't work. The process got killed after 30 mint approx.

Before I was thinking, it may be a memory problem, but I tried on the server (256GB RAM and 16GB GPU) too but unfortunately found same results (i.e. program got killed after running approx. 30 mint).

what could be possible reasons?

Your response will be highly appreciable.

Hi What changes have to be done in this code to allow multi label classification? is there any resource that I can refer to extend your code to allow multi label classification?

I'm a new bee in Sentiment Analysis and recently I'm trying to use CNN to apply to Sentiment Analysis. Yoon's paper helps me a lot and I really appreicate that.

I want to understand every piece of code in this repo, but I get some trouble when I read process_data.py. Variable vocab is a type of dictionary and it should store the frequency of each word occurred in MR datas, which is {word, word_frequency}, but in the function build_data_cv, Yoon used set to store words in each line, which means the duplicate words will be removed, in this case how can we calculate the occurred times of each word ?

    vocab = defaultdict(float)   # dict to store words with its frequences
    with open(pos_file, "rb") as f:
        for line in f:       
            rev = []
            rev.append(line.strip())
            if clean_string:
                orig_rev = clean_str(" ".join(rev))
            else:
                orig_rev = " ".join(rev).lower()
            words = set(orig_rev.split()) # use set to store words, which means duplicate words will be removed in current line

IS THERE ANYBODY CAN HELP ME? THANKS A LOT!!!

I am not familiar with Theano. But it seems in the train_model function, it outputs cost_p, but in the sgd_updates_adadelta function, it optimizes over dropout_cost_p ? ? I am confused on this. Could you please explain this to me if you have time ?

Thanks in advance.