load_data.py
-
bert.py
: BERT embedding -
doc2vec_linear.py
: doc2vec embedding -
doc2vec_tensor.py
: tensor embedding -
doc2vec_infersent.py
: InferSent embedding
-
cluster_doc2vec.py
: doc2vec -
cluster_random.py
: random -
cluster_tensor.py
: tensor embedding -
cluster_infersent.py
: InferSent
-
cnn_joint.py
: CNN -
infersent_joint.py
: InferSent or doc2vec -
bert.py
: BERT -
maml.py
: meta-training,eval_maml.py
: meta-adaption
-
cv_portion.py
: cross validation -
example.sh
: example shell
-
download LDLPackage-v1.2 and tensor_toolbox, which have their own licenses
-
download SemEval Task #14 dataset
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the results of label distribution learning methods, e.g., PT-X, AA-X, SA-X, can be calculated with
LDLPackage-v1.2/edl/cv_portion_ldl.m
-
grid search can be conducted by
cv_grids.py
andLDLPackage-v1.2/edl/cv_grid.m
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a quick demo can be experimented with
bash example.sh
andLDLPackage-v1.2/edl/example.m
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if you want to use a different word embedding, first use
compress_wv.py
to get a compressed word embedding file and put it in the corresponding path -
some codes should be tuned a bit (set the right paths) to work, feel free to contact me if you have any question
-
running the BERT method needs bert-as-service
-
running the InferSent method needs InferSent
GNU GENERAL PUBLIC LICENSE