Click here to download all the ASC datasets (including SemEval 2014, SemEval 2015, SemEval 2016, Twitter, Sentihood, MPQA, Michell and manually-annoted-Hotel).
ContextAvg: the average of the word embeddings is fed to a softmax layer for sentiment prediction, which was adopted as a baseline in [1].
AEContextAvg: the concatenation of the average of the word embeddings and the average of the aspect vectors is fed to a softmax layer for sentiment prediction, which was adopted as a baseline in [1].
LSTM: the last hidden vector obtained by LSTM [2] is used for sentence representation and sentiment prediction.
GRU: the last hidden vector obtained by GRU [3] is used for sentence representation and sentiment prediction.
BiLSTM: the concatenation of last hidden vectors obtained by BiLSTM is used for sentence representation and sentiment prediction.
BiGRU: the concatenation of last hidden vectors obtained by BiGRU is used for sentence representation and sentiment prediction.
TD-LSTM: a target-dependent LSTM model which modeled the preceding and following contexts surrounding the target for sentiment classification [4].
TC-LSTM: this model extends TD-LSTM by incorporating an target con- nection component, which explicitly utilizes the connections between target word and each context word when composing the representation of a sentence. [4].
AT-LSTM: it uses an LSTM to model the sentence and a basic attention mechanism is applied for sentence representation and sentiment prediction. [5].
AT-GRU: it uses a GRU to model the sentence and a basic attention mechanism is applied for sentence representation and sentiment prediction. [5].
AT-BiLSTM: it uses a BiLSTM to model the sentence and a basic attention mechanism is applied for sentence representation and sentiment prediction. [5].
AT-BiGRU: it uses a BiGRU to model the sentence and a basic attention mechanism is applied for sentence representation and sentiment prediction. [5].
ATAE-LSTM: the aspect representation is integrated into attention-based LSTM for sentence representation and sentiment prediction [5].
ATAE-GRU: the aspect representation is integrated into attention-based GRU for sentence representation and sentiment prediction.
ATAE-BiLSTM: the aspect representation is integrated into attention-based BiLSTM for sentence representation and sentiment prediction.
ATAE-BiGRU: the aspect representation is integrated into attention-based BiGRU for sentence representation and sentiment prediction.
IAN: the attentions in the context and aspect were learned interactively for context and aspect representation [6].
LCRS: it contains three LSTMs, i.e., left-, center- and right- LSTM, respectively modeling the three parts of a review (left context, aspect and right context) [7].
CNN: The sentence representation obtained by CNN [8] is used for ASC.
GCAE: it has two separate convolutional layers on the top of the embedding layer, whose outputs are combined by gating units [9].
MemNet: the content and position of the aspect is incorporated into a deep memory network [10].
RAM: a multi-layer architecture where each layer contains an attention-based aggregation of word features and a GRU cell to learn the sentence representation [11].
CABASC: two novel attention mechanisms, namely sentence-level content attention mechanism and context attention mechanism are introduced in a memory network to tackle the semantic-mismatch problem [12].
data/: Store the data
data_orign: the original datasets, including SemEval2014-Task4, SemEval2015-Task12, SemEval2016-Task5, Twitter, Sentihood, Michell, MPQA.
data_processed: the datasets after processing
store: Store the embedding of words, like GloVe.
tmp: store the temporary files.
data_processing/: Processing the data
SemEval2014-Laptop: Processe the Laptop14 dataset.
SemEval2014-Resturant: Process the Restaurants14 dataset.
SemEval2015-Resturant: Process the Restaurants15 dataset.
SemEval2016-Resturant: Process the Restaurants16 dataset.
Twitter: Process the Twitter dataset.
MPQA: Process the MPQA dataset.
Michell-en: Process the Michell-en dataset.
Sentihood: Process the Sentihood dataset.
layers/: Basic units of deep learning models
Attention: Attention units, including ''Contact Attention", ''General Attention" and ''Dot-Product Attention".
Dynamic_RNN: Basic RNN, LSTM and GRU models.
SqueezeEmbedding: Squeeze the embeddings of words.
results/: Store the results
log: Store the log of the models.
ans: Store the answer of the models
attention_weight: Store the weight of the attentions.
model: Store the trained models.
Statics of the performance of the existing works for deep learning based ASC
Method
Restaurants14
Laptop14
Restaurants15
Restaurants16
Twitter
Accuracy
Marco-F1
Accuracy
Marco-F1
Accuracy
Marco-F1
Accuracy
Marco-F1
Accuracy
Marco-F1
RecNN for ASC
AdaRNN
-
-
-
-
-
-
-
-
66.30
65.90
PhraseRNN
66.20
-
-
-
-
-
-
-
-
-
RNN for ASC
GRNN
-
-
-
-
-
-
-
-
-
-
TD-LSTM
-
-
-
-
-
-
-
-
70.80
69.00
TC-LSTM
-
-
-
-
-
-
-
-
71.50
69.50
AE-LSTM
76.60
-
68.90
-
-
-
-
-
-
-
H-LSTM
-
-
-
-
-
-
-
-
-
-
Attention-based RNN for ASC
ATAE-LSTM
77.20
-
68.70
-
-
-
-
-
-
-
AB-LSTM
-
-
-
-
-
-
-
-
72.60
72.20
BILSTM-ATT-G
-
-
-
-
-
-
-
-
73.60
72.10
IAN
78.60
-
72.10
-
-
-
-
-
-
-
AF-LSTM(CONV)
75.44
-
68.81
-
-
-
-
-
-
-
HEAT
-
-
-
-
-
-
-
-
-
-
Sentic LSTM+TA+SA
-
-
-
-
-
-
-
-
-
-
PRET+MULT
79.11
79.73
71.15
67.46
81.30
68.74
85.58
79.76
-
-
PBAN
81.16
-
74.12
-
-
-
-
-
-
-
LSTM+SynATT+TarRep
80.63
71.32
71.94
69.23
81.67
66.05
84.61
67.45
-
-
MGAN
81.25
71.94
75.39
72.47
-
-
-
-
72.54
70.81
Inter-Aspect Dependencies
79.00
-
72.50
-
-
-
-
-
-
-
AOA-LSTM
81.20
-
74.50
-
-
-
-
-
-
-
LCR-Rot
81.34
-
75.24
-
-
-
-
-
72.69
-
Word&Clause-Level ATT
-
-
-
-
80.90
68.50
-
-
-
-
CNN for ASC
GCAE
77.28
-
69.14
-
-
-
-
-
-
-
PF-CNN
79.20
-
70.06
-
-
-
-
-
-
-
Conv-Memnet
78.26
68.38
76.37
72.10
-
-
-
-
72.11
70.80
TNet
80.69
71.27
76.54
71.75
-
-
-
-
74.97
73.60
Memory Network for ASC
MemNet
80.95
-
72.21
-
-
-
-
-
-
-
DyMemNN
-
58.82
-
60.11
-
-
-
-
-
-
RAM
80.23
70.80
74.49
71.35
-
-
-
-
69.36
73.85
CEA
80.98
-
72.88
-
-
-
-
-
-
-
DAuM
82.32
71.45
74.45
70.16
-
-
-
-
72.14
60.24
IARM
80.00
-
73.8
-
-
-
-
-
-
-
TMNs
-
68.84
-
67.23
-
-
-
-
-
-
Cabasc
80.89
-
75.07
-
-
-
-
-
71.53
-
The results of our implemented models
The results of dataset Restaurants14
Accuracy
Macro
Micro
Precision
Recall
F1
Precision
Recall
F1
Precision
Recall
F1
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
ContextAvg
73.48
62.92
58.44
59.58
73.48
73.48
73.48
56.48
51.79
80.49
55.61
29.59
90.11
56.04
37.66
85.03
AEContextAvg
75.27
66.30
61.47
63.10
75.27
75.27
75.27
62.09
55.47
81.36
57.65
36.22
90.52
59.79
43.83
85.70
LSTM
77.23
67.54
64.34
65.51
77.23
77.23
77.23
63.35
54.55
84.73
61.73
39.80
91.48
62.53
46.02
87.98
GRU
78.75
70.51
65.61
67.11
78.75
78.75
78.75
67.36
59.84
84.35
66.33
37.24
93.27
66.84
45.91
88.58
BiGRU
77.14
67.61
63.55
65.15
77.14
77.14
77.14
64.94
53.69
84.19
57.65
40.82
92.17
61.08
46.38
88.00
BiLSTM
78.30
69.11
66.01
67.12
78.30
78.30
78.30
65.13
56.64
85.55
64.80
41.33
91.90
64.96
47.79
88.61
TD-LSTM
78.66
70.84
67.56
68.98
78.66
78.66
78.66
72.88
54.55
85.09
65.82
45.92
90.93
69.17
49.86
87.92
TC-LSTM
77.41
69.06
65.18
66.72
77.41
77.41
77.41
67.78
55.70
83.69
62.24
42.35
90.93
64.89
48.12
87.16
AT-LSTM
78.04
70.84
61.52
63.37
78.04
78.04
78.04
70.06
61.25
81.23
63.27
25.00
96.29
66.49
35.51
88.12
AT-GRU
78.30
70.74
64.76
66.58
78.30
78.30
78.30
67.91
61.21
83.11
64.80
36.22
93.27
66.32
45.51
87.90
AT-BiGRU
77.77
69.51
64.74
66.18
77.77
77.77
77.77
65.13
59.84
83.56
64.80
37.24
92.17
64.96
45.91
87.66
AT-BiLSTM
78.84
72.84
63.67
65.66
78.84
78.84
78.84
68.45
67.82
82.27
65.31
30.10
95.60
66.84
41.70
88.44
ATAE-GRU
76.79
68.68
63.49
65.32
76.79
76.79
76.79
69.49
54.62
81.92
62.76
36.22
91.48
65.95
43.56
86.44
ATAE-LSTM
76.79
67.93
62.74
63.72
76.79
76.79
76.79
64.53
57.00
82.25
66.84
29.08
92.31
65.66
38.51
86.99
ATAE-BiGRU
76.34
65.95
63.26
63.82
76.34
76.34
76.34
63.77
50.41
83.67
67.35
31.63
90.80
65.51
38.87
87.09
ATAE-BiLSTM
75.98
67.01
61.71
63.43
75.98
75.98
75.98
66.29
53.28
81.46
60.20
33.16
91.76
63.10
40.88
86.30
IAN
76.70
68.29
63.69
65.12
76.70
76.70
76.70
64.25
58.06
82.57
63.27
36.73
91.07
63.75
45.00
86.61
LCRS
76.25
68.71
60.85
63.03
76.25
76.25
76.25
69.82
56.44
79.88
60.20
29.08
93.27
64.66
38.38
86.06
CNN
75.18
68.45
58.44
60.25
75.18
75.18
75.18
60.44
65.79
79.12
56.12
25.51
93.68
58.20
36.76
85.79
GCAE
77.41
68.58
64.80
65.06
77.41
77.41
77.41
64.86
57.43
83.44
73.47
29.59
91.35
68.90
39.06
87.21
MemNet
73.39
62.74
61.13
61.09
73.39
73.39
73.39
52.56
52.38
83.29
62.76
33.67
86.95
57.21
40.99
85.08
RAM
77.41
68.38
65.67
66.76
77.41
77.41
77.41
67.20
53.25
84.68
64.80
41.84
90.38
65.97
46.86
87.44
CABASC
77.68
69.01
67.18
68.02
77.68
77.68
77.68
65.59
55.68
85.75
62.24
50.00
89.29
63.87
52.69
87.48
The results of Laptop14
Accuracy
Macro
Micro
Precision
Recall
F1
Precision
Recall
F1
Precision
Recall
F1
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
ContextAvg
66.93
63.47
59.98
58.19
66.93
66.93
66.93
46.41
67.65
76.35
65.62
27.22
87.10
54.37
38.82
81.37
AEContextAvg
66.46
61.64
59.56
58.04
66.46
66.46
66.46
47.40
61.54
75.97
64.06
28.40
86.22
54.49
38.87
80.77
LSTM
66.14
62.37
60.20
55.35
66.14
66.14
66.14
48.08
62.79
76.23
78.12
15.98
86.51
59.52
25.47
81.04
GRU
67.71
64.31
61.50
58.60
67.71
67.71
67.71
49.47
66.67
76.80
73.44
23.67
87.39
59.12
34.93
81.76
BiGRU
69.44
65.61
63.83
61.49
69.44
69.44
69.44
49.22
67.11
80.49
74.22
30.18
87.10
59.19
41.63
83.66
BiLSTM
68.81
63.41
63.56
62.09
68.81
68.81
68.81
50.28
59.05
80.90
69.53
36.69
84.46
58.36
45.26
82.64
TD-LSTM
68.50
62.66
62.98
61.87
68.50
68.50
68.50
47.70
57.63
82.66
64.84
40.24
83.87
54.97
47.39
83.26
TC-LSTM
67.08
62.02
62.66
61.11
67.08
67.08
67.08
46.52
57.76
81.79
67.97
39.64
80.35
55.24
47.02
81.07
AT-LSTM
69.44
64.23
65.02
63.16
69.44
69.44
69.44
51.91
58.88
81.90
74.22
37.28
83.58
61.09
45.65
82.73
AT-GRU
70.85
66.57
66.21
63.58
70.85
70.85
70.85
54.21
64.63
80.87
80.47
31.36
86.80
64.78
42.23
83.73
AT-BiGRU
69.28
64.44
64.36
63.28
69.28
69.28
69.28
48.86
62.61
81.84
67.19
42.60
83.28
56.58
50.70
82.56
AT-BiLSTM
71.94
66.36
66.80
66.42
71.94
71.94
71.94
55.48
59.06
84.55
63.28
52.07
85.04
59.12
55.35
84.80
ATAE-GRU
69.75
64.43
63.46
62.45
69.75
69.75
69.75
52.76
61.22
79.31
67.19
35.50
87.68
59.11
44.94
83.29
ATAE-LSTM
67.40
65.16
62.18
58.47
67.40
67.40
67.40
47.39
69.64
78.44
78.12
23.08
85.34
59.00
34.67
81.74
ATAE-BiGRU
70.38
67.00
66.20
64.12
70.38
70.38
70.38
49.25
68.82
82.95
76.56
37.87
84.16
59.94
48.85
83.55
ATAE-BiLSTM
70.53
66.84
65.99
63.43
70.53
70.53
70.53
50.75
67.47
82.30
78.91
33.14
85.92
61.77
44.44
84.07
IAN
68.50
64.11
62.69
60.90
68.50
68.50
68.50
51.12
63.41
77.78
71.09
30.77
86.22
59.48
41.43
81.78
LCRS
66.46
63.15
60.84
59.50
66.46
66.46
66.46
46.70
66.67
76.08
66.41
33.14
82.99
54.84
44.27
79.38
CNN
66.93
65.95
59.91
57.75
66.93
66.93
66.93
45.99
76.36
75.51
67.19
24.85
87.68
54.60
37.50
81.14
GCAE
65.83
60.95
60.34
59.20
65.83
65.83
65.83
43.72
60.00
79.14
62.50
37.28
81.23
51.45
45.99
80.17
MemNet
64.42
59.08
59.36
58.01
64.42
64.42
64.42
43.01
54.87
79.35
62.50
36.69
78.89
50.96
43.97
79.12
RAM
67.55
62.25
60.78
59.73
67.55
67.55
67.55
49.09
60.44
77.23
63.28
32.54
86.51
55.29
42.31
81.60
CABASC
70.06
66.14
63.05
62.94
70.06
70.06
70.06
50.98
69.79
77.63
60.94
39.64
88.56
55.52
50.57
82.74
The results of Restaurants15
Accuracy
Macro
Micro
Precision
Recall
F1
Precision
Recall
F1
Precision
Recall
F1
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
ContextAvg
72.31
65.35
50.18
49.80
72.31
72.31
72.31
74.91
50.00
71.15
58.67
2.22
89.65
65.80
4.26
79.34
AEContextAvg
73.37
49.87
50.22
49.17
73.37
73.37
73.37
78.54
0.00
71.06
59.25
0.00
91.41
67.55
0.00
79.96
LSTM
77.99
51.77
54.60
53.14
77.99
77.99
77.99
75.49
0.00
79.84
78.32
0.00
85.46
76.88
0.00
82.55
GRU
76.80
51.87
53.01
51.96
76.80
76.80
76.80
80.97
0.00
74.64
67.63
0.00
91.41
73.70
0.00
82.18
BiGRU
77.28
51.48
53.70
52.44
77.28
77.28
77.28
76.99
0.00
77.46
72.54
0.00
88.55
74.70
0.00
82.63
BiLSTM
78.34
52.34
54.36
53.14
78.34
78.34
78.34
79.18
0.00
77.84
72.54
0.00
90.53
75.72
0.00
83.71
TD-LSTM
77.28
64.53
57.65
59.04
77.28
77.28
77.28
78.06
37.50
78.04
71.97
13.33
87.67
74.89
19.67
82.57
TC-LSTM
74.44
62.62
53.41
54.10
74.44
74.44
74.44
76.51
37.50
73.84
65.90
6.67
87.67
70.81
11.32
80.16
AT-LSTM
80.00
53.32
55.82
54.48
80.00
80.00
80.00
79.88
0.00
80.08
78.03
0.00
89.43
78.95
0.00
84.50
AT-GRU
79.41
52.87
55.48
54.11
79.41
79.41
79.41
78.78
0.00
79.84
78.32
0.00
88.11
78.55
0.00
83.77
AT-BiGRU
77.99
61.04
54.64
54.30
77.99
77.99
77.99
81.88
25.00
76.24
70.52
2.22
91.19
75.78
4.08
83.05
AT-BiLSTM
79.88
53.11
55.88
54.45
79.88
79.88
79.88
78.41
0.00
80.93
79.77
0.00
87.89
79.08
0.00
84.27
ATAE-GRU
78.58
85.80
55.40
54.88
78.58
78.58
78.58
79.33
100.00
78.06
75.43
2.22
88.55
77.33
4.35
82.97
ATAE-LSTM
79.53
53.15
55.34
54.09
79.53
79.53
79.53
80.62
0.00
78.85
75.72
0.00
90.31
78.09
0.00
84.19
ATAE-BiGRU
78.70
69.08
56.30
56.29
78.70
78.70
78.70
77.46
50.00
79.80
77.46
4.44
87.00
77.46
8.16
83.25
ATAE-BiLSTM
78.34
52.21
54.59
53.29
78.34
78.34
78.34
78.14
0.00
78.47
75.43
0.00
88.33
76.76
0.00
83.11
IAN
79.41
86.18
56.74
56.82
79.41
79.41
79.41
78.82
100.00
79.72
77.46
4.44
88.33
78.13
8.51
83.80
LCRS
75.50
59.03
53.63
53.73
75.50
75.50
75.50
76.28
25.00
75.81
68.79
4.44
87.67
72.34
7.55
81.31
CNN
69.35
64.71
47.47
46.93
69.35
69.35
69.35
77.46
50.00
66.67
47.69
2.22
92.51
59.03
4.26
77.49
GCAE
76.33
57.61
53.89
53.32
76.33
76.33
76.33
75.07
20.00
77.76
73.99
2.22
85.46
74.53
4.00
81.43
MemNet
76.45
71.93
56.34
57.97
76.45
76.45
76.45
76.90
62.50
76.39
70.23
11.11
87.67
73.41
18.87
81.64
RAM
76.21
51.23
52.71
51.62
76.21
76.21
76.21
79.00
0.00
74.68
68.50
0.00
89.65
73.37
0.00
81.48
CABASC
76.21
61.73
56.28
57.30
76.21
76.21
76.21
76.47
31.25
77.47
71.39
11.11
86.34
73.84
16.39
81.67
The results of Restaurants16
Accuracy
Macro
Micro
Precision
Recall
F1
Precision
Recall
F1
Precision
Recall
F1
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
ContextAvg
80.56
49.61
52.56
51.04
80.56
80.56
80.56
61.82
0.00
87.01
66.67
0.00
91.00
64.15
0.00
88.96
AEContextAvg
80.79
49.87
52.99
51.37
80.79
80.79
80.79
62.33
0.00
87.26
68.14
0.00
90.83
65.11
0.00
89.01
LSTM
83.12
76.89
59.24
58.23
83.12
83.12
83.12
64.23
75.00
91.43
81.86
6.82
89.03
71.98
12.50
90.22
GRU
83.47
69.29
60.53
61.34
83.47
83.47
83.47
67.81
50.00
90.07
77.45
13.64
90.51
72.31
21.43
90.29
BiGRU
83.47
77.36
59.66
61.39
83.47
83.47
83.47
68.18
75.00
88.91
73.53
13.64
91.82
70.75
23.08
90.34
BiLSTM
82.54
52.03
53.70
52.81
82.54
82.54
82.54
69.70
0.00
86.38
67.65
0.00
93.45
68.66
0.00
89.78
TD-LSTM
84.17
52.67
57.08
54.70
84.17
84.17
84.17
67.22
0.00
90.78
79.41
0.00
91.82
72.81
0.00
91.29
TC-LSTM
82.07
55.80
54.73
54.06
82.07
82.07
82.07
66.82
12.50
88.07
70.10
2.27
91.82
68.42
3.85
89.90
AT-LSTM
82.77
51.85
55.44
53.56
82.77
82.77
82.77
67.11
0.00
88.43
75.00
0.00
91.33
70.83
0.00
89.86
AT-GRU
83.82
52.68
56.04
54.30
83.82
83.82
83.82
69.06
0.00
88.99
75.49
0.00
92.64
72.13
0.00
90.78
AT-BiGRU
83.47
77.57
57.55
58.06
83.47
83.47
83.47
69.44
75.00
88.26
73.53
6.82
92.31
71.43
12.50
90.24
AT-BiLSTM
82.89
85.01
58.75
56.88
82.89
82.89
82.89
63.20
100.00
91.84
83.33
4.55
88.38
71.88
8.70
90.08
ATAE-GRU
82.31
51.16
55.11
53.01
82.31
82.31
82.31
64.41
0.00
89.09
74.51
0.00
90.83
69.09
0.00
89.95
ATAE-LSTM
82.19
51.70
53.10
52.33
82.19
82.19
82.19
69.07
0.00
86.02
65.69
0.00
93.62
67.34
0.00
89.66
ATAE-BiGRU
82.54
84.85
57.17
56.33
82.54
82.54
82.54
65.42
100.00
89.14
76.96
4.55
90.02
70.72
8.70
89.58
ATAE-BiLSTM
83.35
78.88
58.85
59.36
83.35
83.35
83.35
67.24
80.00
89.39
76.47
9.09
91.00
71.56
16.33
90.19
IAN
82.19
73.57
57.66
56.30
82.19
82.19
82.19
64.17
66.67
89.87
79.90
4.55
88.54
71.18
8.51
89.20
LCRS
81.61
68.60
57.16
59.36
81.61
81.61
81.61
70.31
50.00
85.50
66.18
13.64
91.65
68.18
21.43
88.47
CNN
81.84
73.19
55.21
55.14
81.84
81.84
81.84
65.44
66.67
87.48
69.61
4.55
91.49
67.46
8.51
89.44
GCAE
79.98
49.95
50.00
49.74
79.98
79.98
79.98
66.47
0.00
83.38
56.37
0.00
93.62
61.01
0.00
88.20
MemNet
81.26
67.07
55.25
57.94
81.26
81.26
81.26
70.41
46.15
84.64
58.33
13.64
93.78
63.81
21.05
88.98
RAM
83.47
52.61
55.12
53.83
83.47
83.47
83.47
70.00
0.00
87.83
72.06
0.00
93.29
71.01
0.00
90.48
CABASC
83.12
52.33
54.63
53.44
83.12
83.12
83.12
69.57
0.00
87.42
70.59
0.00
93.29
70.07
0.00
90.26
The results of Twitter
Accuracy
Macro
Micro
Precision
Recall
F1
Precision
Recall
F1
Precision
Recall
F1
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
Neg.
Neu.
Pos.
ContextAvg
68.35
68.69
64.26
65.82
68.35
68.35
68.35
70.15
67.88
68.03
54.34
80.64
57.80
61.24
73.71
62.50
AEContextAvg
69.94
69.57
66.57
67.75
69.94
69.94
69.94
67.11
70.66
70.95
58.96
80.06
60.69
62.77
75.07
65.42
LSTM
69.22
69.64
65.13
66.52
69.22
69.22
69.22
66.46
69.12
73.33
63.01
81.50
50.87
64.69
74.80
60.07
GRU
68.79
67.37
68.11
67.71
68.79
68.79
68.79
64.32
73.80
64.00
68.79
70.81
64.74
66.48
72.27
64.37
BiGRU
67.20
67.63
62.14
63.68
67.20
67.20
67.20
67.11
66.74
69.03
58.96
82.37
45.09
62.77
73.74
54.55
BiLSTM
68.21
67.75
64.84
65.98
68.21
68.21
68.21
69.18
69.13
64.94
58.38
78.32
57.80
63.32
73.44
61.16
TD-LSTM
71.82
72.21
68.11
68.67
71.82
71.82
71.82
65.15
73.59
77.88
74.57
82.95
46.82
69.54
77.99
58.48
TC-LSTM
72.69
72.76
69.65
70.90
72.69
72.69
72.69
74.00
72.56
71.71
64.16
81.79
63.01
68.73
76.90
67.08
AT-LSTM
70.95
69.94
69.17
69.52
70.95
70.95
70.95
69.01
73.54
67.28
68.21
76.30
63.01
68.60
74.89
65.07
AT-GRU
70.66
71.21
66.47
67.97
70.66
70.66
70.66
70.00
70.07
73.55
64.74
83.24
51.45
67.27
76.09
60.54
AT-BiGRU
71.97
75.33
67.73
69.62
71.97
71.97
71.97
89.00
69.93
67.05
51.45
84.68
67.05
65.20
76.60
67.05
AT-BiLSTM
69.80
68.93
67.73
68.14
69.80
69.80
69.80
70.55
72.65
63.59
59.54
76.01
67.63
64.58
74.29
65.55
ATAE-GRU
69.94
70.11
65.51
67.11
69.94
69.94
69.94
68.97
69.73
71.64
57.80
83.24
55.49
62.89
75.89
62.54
ATAE-LSTM
68.64
68.86
65.22
66.60
68.64
68.64
68.64
69.54
68.25
68.79
60.69
78.90
56.07
64.81
73.19
61.78
ATAE-BiGRU
70.23
71.31
66.28
68.07
70.23
70.23
70.23
72.99
68.60
72.34
57.80
82.08
58.96
64.52
74.74
64.97
ATAE-BiLSTM
70.95
72.77
66.38
68.38
70.95
70.95
70.95
80.34
69.10
68.87
54.34
84.68
60.12
64.83
76.10
64.20
IAN
71.82
73.00
67.15
69.11
71.82
71.82
71.82
76.52
70.21
72.26
58.38
85.84
57.23
66.23
77.24
63.87
LCRS
68.06
67.63
64.93
65.96
68.06
68.06
68.06
70.00
69.25
63.64
56.65
77.46
60.69
62.62
73.12
62.13
CNN
67.77
66.41
64.26
65.02
67.77
67.77
67.77
66.67
70.94
61.63
53.18
78.32
61.27
59.16
74.45
61.45
GCAE
72.11
72.12
70.04
70.85
72.11
72.11
72.11
75.69
72.65
68.00
63.01
78.32
68.79
68.77
75.38
68.39
MemNet
69.65
69.09
66.76
67.68
69.65
69.65
69.65
71.17
70.57
65.52
67.05
78.32
54.91
69.05
74.25
59.75
RAM
70.09
71.32
64.93
66.48
70.09
70.09
70.09
70.62
68.84
74.51
65.32
85.55
43.93
67.87
76.29
55.27
CABASC
68.64
69.74
64.64
66.44
68.64
68.64
68.64
75.00
67.07
67.14
58.96
80.64
54.34
66.02
73.23
60.06
References
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