Non-Linear Sub-Space Embedding model used for the SemEval 2015 challenge described in
R. F. Astudillo, S. Amir, W. Ling, B. Martins, M. Silva and I. Trancoso "INESC-ID:
Sentiment Analysis without hand-coded Features or Liguistic Resources using Embedding
Subspaces", SemEval 2015
For the extended experiments, including POS tagging, please cite
R. F. Astudillo, S. Amir, W. Ling, M. Silva and I. Trancoso "Learning Word Representations
from Scarce and Noisy Data with Embedding Sub-spaces", ACL-IJCNLP 2015
The code for these two papers is available in the semeval2015 branch.
Current master points to the extended version for SemEval 2016, for this
cite,
Amir, Silvio, Ramón Astudillo, Wang Ling, Mário J. Silva, and Isabel Trancoso. "INESC-ID at
SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words." SemEval 2016.
The code uses Python2 and theano. I recommend a virtual environment and upgrading to latest install tools
virtualenv venv
source venv/bin/activate
pip install pip --upgrade
pip install setuptools --upgrade
Then (or otherwise) just install in two steps
pip install -r requirements.txt
python setup.py develop
You will also need twokenize. Since there is no installer you will have to download it and store it on the root folder of this repo, on unix just do
wget https://raw.githubusercontent.com/myleott/ark-twokenize-py/master/twokenize.py
from the root folder of this repository. This should create the file
twokenize.py
The go.sh bash script will need cygwin or equivalent in Windows machines, but you can run the python commands inside the script on a Windows machine directly. See inside the script for step by step details.
To reproduce the paper's results you will need the SemEval data from 2013 to 2016. This is not public so you have to ask someone for it (Silvio and I have it). The paths for the data should be
DATA/txt/Twitter2013_raws.txt
DATA/txt/Twitter2014_raws.txt
DATA/txt/Twitter2015_raws.txt
DATA/txt/Twitter2016_raws.txt
DATA/txt/semeval_train2016.txt
The data should look this example
id1 id2 neutral None @USER i told you shane would get his 5th-star on rivals before signing day . @USER
You will also need to use some pre-trained embeddings. You can find the embeddings we used here
https://www.l2f.inesc-id.pt/~wlin/public/embeddings/
For example to download the 400 side embeddings (used by default in the go script)
wget https://www.l2f.inesc-id.pt/~wlin/public/embeddings/struc_skip_400.txt -P DATA/txt/
This should store the file as
DATA/txt/struc_skip_400.txt
Once you installed and obtained the data, just call
./go.sh
to extract the data and train the model. If you are using the largest embeddings and you have no GPU, this might take a while. You can find more details about the steps taken in the script.
If everything goes fine you should get following results, which are slightly above those reported on the last paper.
| Test-Set | Accuracy | F-measure (sum) |
|---|---|---|
| 2013 | 72.82% | 0.724 |
| 2014 | 73.87% | 0.723 |
| 2015 | 68.19% | 0.658 |
| 2016 | 61.36% | 0.615 |
To train the embeddings with other data you can use
https://github.com/wlin12/JNN
In case you want to use other embeddings the format is the following: The first line should contain
<number of words> <embedding size>
After that, the embedding for each word is specified as
<word> <embedding_vector>
<embedding_vector> where each float is separated by a white space.
As you may know theano is no more longer supported. It was a great start for
Computational Graph toolkits but there are much better alternatives out there
nowadays (Pytorch, TensorFlow). If you have problems running it, try disabling
cuDNN. For this you need to write the following on your ~/.theanorc
[dnn]
enabled = False
If you are a newtimer to theano, to get it running on the GPU the faster way is
to define this on your ~/.bashrc
export THEANO_FLAGS='cuda.root=/usr/local/cuda-<version>/,device=gpu,floatX=float32'
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda-<version>/lib64"
where <version> is the CUDA version.