ConvNets For Sentence Classification Save
"Convolutional Neural Networks for Sentence Classification" (Kim 2014) - https://www.aclweb.org/anthology/D14-1181
Sentence Classification with Convolution Neural Networks
This repository contains experiments done with Keras implementations of the Convolutional Neural Networks (ConvNets) to perform sentence classification based on the paper of Kim (2014).
The idea was not to replicate the results reported on the paper, but to have a first hands-on experiment with Keras, and some understanding of the method for classification proposed in the paper.
This code is also the result of a blog post I wrote about the idea of using ConvNets to classify sentences:
Experiments
- TREC (http://cogcomp.cs.illinois.edu/Data/QA/QC/)
- Stanford Sentiment Treebank (https://nlp.stanford.edu/sentiment/) (fine-grained-labels)
Some considerations:
- I used GloVe vectors of dimensionality 50
- The random embeddings have a dimensionality of 300
- I ran the training for 10 epochs
Accuracy
| Model | TREC | SST-1 |
|---|---|---|
| CNN w/ random word embeddings | 0.918 | 0.432 |
| CNN w/ pre-trained static word embeddings | 0.896 | 0.484 |
| CNN w/ pre-trained dynamic word embeddings | 0.919 | 0.432 |
| CNN multichannel dynamic and static word embeddings | 0.921 | 0.406 |
The results are in line with the ones reported in the paper, a bit lower, but within the expected.
Precision / Recall / F1
Since this is a multi-label dataset I also report the results per class for each of the different models.
TREC
CNN w/ random word embeddings
precision recall f1-score support
ABBR 1.00 0.22 0.36 9
DESC 0.74 0.22 0.34 138
ENTY 0.55 0.88 0.68 94
HUM 0.84 0.86 0.85 65
LOC 0.91 0.93 0.92 81
NUM 0.61 0.84 0.70 113
CNN w/ pre-trained static word embeddings
precision recall f1-score support
ABBR 1.00 0.56 0.71 9
DESC 0.57 0.09 0.15 138
ENTY 0.32 0.89 0.47 94
HUM 0.94 0.69 0.80 65
LOC 0.75 0.73 0.74 81
NUM 0.98 0.71 0.82 113
CNN w/ pre-trained dynamic word embeddings
precision recall f1-score support
ABBR 0.75 0.67 0.71 9
DESC 0.76 0.22 0.35 138
ENTY 0.39 0.82 0.52 94
HUM 0.94 0.77 0.85 65
LOC 0.74 0.90 0.82 81
NUM 0.96 0.85 0.90 113
CNN multichannel dynamic and static word embeddings
precision recall f1-score support
ABBR 0.88 0.78 0.82 9
DESC 0.64 0.26 0.37 138
ENTY 0.38 0.79 0.51 94
HUM 0.84 0.83 0.84 65
LOC 0.87 0.89 0.88 81
NUM 1.00 0.81 0.90 113
SST-1
CNN w/ random word embeddings
precision recall f1-score support
negative 0.22 0.19 0.20 405
neutral 0.53 0.72 0.61 1155
positive 0.19 0.11 0.14 424
very positive 0.07 0.02 0.03 112
very_negative 0.05 0.01 0.01 114
CNN w/ pre-trained static word embeddings
precision recall f1-score support
negative 0.13 0.03 0.05 405
neutral 0.52 0.91 0.66 1155
positive 0.13 0.03 0.05 424
very positive 0.00 0.00 0.00 112
very_negative 0.00 0.00 0.00 114
CNN w/ pre-trained dynamic word embeddings
precision recall f1-score support
negative 0.15 0.10 0.12 405
neutral 0.52 0.76 0.61 1155
positive 0.18 0.08 0.11 424
very positive 0.12 0.03 0.04 112
very_negative 0.04 0.01 0.01 114
CNN multichannel dynamic and static word embeddings
precision recall f1-score support
negative 0.16 0.11 0.13 405
neutral 0.52 0.65 0.58 1155
positive 0.23 0.22 0.22 424
very positive 0.02 0.01 0.01 112
very_negative 0.12 0.03 0.04 114
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