This repo has multiple sequential models for aspect extraction from product reviews.
If the code is useful in your research, please cite the following paper:
Poria, S., Cambria, E. and Gelbukh, A., 2016. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, pp.42-49.
Given a sentence, the task is to extract aspects. Here is an example
I like the battery life of this phone"
Converting this sentence to IOB would look like this -
I O
like O
the O
battery B-A
life I-A
of O
this O
phone O
Similar to Ma and Hovy.
- concatenate final states of a bi-lstm on character embeddings to get a character-based representation of each word
- concatenate this representation to a standard word vector representation (GloVe here)
- run a bi-lstm on each sentence to extract contextual representation of each word
- decode with a linear chain CRF
Similar to [Collobert et al.] (http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf)
- form a window around the word to tag
- apply MLP on that window
- obtain logits
- apply viterbi (CRF) for sequence tagging
Similar to Poria et al.
- form a window around the word to tag
- apply CNN on that window
- apply maxpool on that window (Caution: different from global maxpool)
- obtain logits
- apply CRF for sequence tagging
python xmlToIOB.py
Download Glove embeddings (GloVe: http://nlp.stanford.edu/data/glove.840B.300d.zip )
- [DO NOT MISS THIS STEP] Build vocab from the data and extract trimmed glove vectors according to the config in
model/config.py.
python build_data.py
- Train the model with
python train.py
- Evaluate and interact with the model with
python evaluate.py
Data iterators and utils are in model/data_utils.py and the model with training/test procedures is in model/aspect_model.py
The training data must be in the following format (identical to the CoNLL2003 dataset).
A default test file is provided to help you getting started.
The O
duck B-A
confit I-A
is O
always O
amazing O
and O
the O
foie B-A
gras I-A
terrine I-A
with I-A
figs I-A
was O
out O
of O
this O
world O
The O
wine B-A
list I-A
is O
interesting O
and O
has O
many O
good O
values O
Once you have produced your data files, change the parameters in config.py like
# dataset
filename_train = "data/ABSA16_Restaurants_Train_SB1_v2_mod.iob"
filename_dev = "data/EN_REST_SB1_TEST_2016_mod.iob"
filename_test = "data/EN_REST_SB1_TEST_2016_mod.iob"
Chunk based evaluation
Laptop 2014 -> F1 - 79.93
Restaurant 2014 -> F1 - 84.22
Partial matching based evaluation
Laptop 2014 -> F1 - 86.84
Restaurant 2014 -> F1 - 88.42
This project is licensed under the terms of the apache 2.0 license (as Tensorflow and derivatives). If used for research, citation would be appreciated.