Code for our ACL 2018 paper "Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction"
Label "The retina display is great ." as "O B I O O O" so to extract "retina display" as an aspect. Check this article for aspect-based sentiment analysis or this for domain representation learning.
All code are tested under python 3.6.2 + pytorch 0.2.0_4
Step 1: Download general embeddings (GloVe: http://nlp.stanford.edu/data/glove.840B.300d.zip ), save it in data/embedding/gen.vec
Step 2: Download Domain Embeddings (You can find the link under this paper's title in https://www.cs.uic.edu/~hxu/ ), save them in data/embedding
Step 3: Download and install fastText (https://github.com/facebookresearch/fastText) to fastText/
Step 4: Download official datasets to data/official_data/
Download official evaluation scripts to script/
We assume the following file names.
SemEval 2014 Laptop (http://alt.qcri.org/semeval2014/task4/):
data/official_data/Laptops_Test_Data_PhaseA.xml
data/official_data/Laptops_Test_Gold.xml
script/eval.jar
SemEval 2016 Restaurant (http://alt.qcri.org/semeval2016/task5/)
data/official_data/EN_REST_SB1_TEST.xml.A
data/official_data/EN_REST_SB1_TEST.xml.gold
script/A.jar
Step 5: Run prep_embedding.py to build numpy files for general embeddings and domain embeddings.
python script/prep_embedding.py
Step 6: Fill in out-of-vocabulary (OOV) embedding
./fastText/fasttext print-word-vectors data/embedding/laptop_emb.vec.bin < data/prep_data/laptop_emb.vec.oov.txt > data/prep_data/laptop_oov.vec
./fastText/fasttext print-word-vectors data/embedding/restaurant_emb.vec.bin < data/prep_data/restaurant_emb.vec.oov.txt > data/prep_data/restaurant_oov.vec
python script/prep_oov.py
Step 7: Train the laptop model
python script/train.py
Train the restaurant model
python script/train.py --domain restaurant
Step 8: Evaluate Laptop dataset
python script/evaluation.py
Evaluate Restaurant dataset
python script/evaluation.py --domain restaurant
If you find our code useful, please cite our paper.
@InProceedings{xu_acl2018,
author = {Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.},
title = {Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction},
booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics},
publisher = {Association for Computational Linguistics},
year = {2018}
}