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University of Chittagong
February 12, 2020
Md. Ahasan Ullah
Department of Computer Science & Engineering
A Gated Recurrent Neural Network-based Approach for
Detecting Suggestions
ID: 16701019
Supervisor Name: Dr. Abu Nowshed Chy
Problem Statement
2
Determine whether a user review contains any informative suggestion
Not Suggestion
That hotel’s food was so
delicious.
Should have a juice glass in
the roof
Suggestion
User Review
Suggestion Detecting
System
Motivation
3
❖ With the ever growing availability of opinions and reviews on the
web, suggestion mining has become a popular research area
❖ Mining suggestions from huge review posts is beneficial to
address several real-world problems
Related Work
4
 Domain adversarial neural networks for domain adaptation in suggestion
mining (Mateusz et al., SemEval-2019)
 Expert-informed pattern recognition for suggestion mining (Nelleke et al.,
SemEval-2019)
 A stacked bi-lstm model for suggestion mining classification (Ding et al.,
SemEval-2019)
 Suggestion mining using svm with handcrafted features (Ilia et al.,
SemEval-2019)
 Mining suggestions from online reviews using deep learning techniques on
augmented data (Rajalakshmi et al., SemEval-2019)
 Exploring machine learning approaches in classifying text as suggestion or
non-suggestion (Tirana et al., SemEval-2019)
Methodology (Proposed Architecture)
5
L × D
L = Text Length
D = Word Vector Dimension
Text Embeddings
CNN
Methodology
6
Convolutional Neural Network Architecture:
Sentence Matrix
Convolutional Layer
Feature vector
G
R
U
Max-pulling layer
Final class
Methodology
7
Gated Recurrent Units (GRU) Architecture:
Methodology
8
Characteristics of GRU:
❖ Has two gates
▪ Reset
▪ Update
❖ No memory Unit
Advantages of GRU:
❖ Works faster in less training data
❖ Easy to implement
Characteristics of CNN:
❖ Has four layers
▪ Convolutional
▪ Pooling
▪ ReLU correction
▪ Fully-connected
❖ Works with filters
Advantages of CNN:
❖ Can be used in both image and
text
❖ A very good feature extractor
Result
9
Dataset Description:
❖ SemEval 2019 Task 9 - Suggestion Mining from Online Reviews
and Forums
❖ Dataset Statistics:
➢ Training Data: 8300 reviews
➢ Test Data: 833 reviews
Performance with Various Filter Sizes:
Result
10
Method F Score
Li and Ding, 2019 (Ensemble Classifier, Attention-based-
LSTM, TextCNN, C-LSTM, Bi-LSTM. Word2Vec)
0.6776
Our Proposed (2DCNN-GRU) 0.5806
Ding et al., 2019 (BiLSTM, LSTM. Word2Vec, GloVe) 0.5659
Markov and De la Clergerie, 2019 (SVM, Logistic
Regression, Hand-crafted Features)
0.5118
Fatyanosa et al., 2019 (SVM, Linear Regression, Naive
Bayes, CNN, GloVe )
0.4730
Ahmed et al., 2019 (Containment Similarity, Maximum
Common Subgraph, Tree-based Pipeline Optimization Tool)
0.3537
Comparative Performance on Suggestion Mining Dataset
Result
11
0.4
0.45
0.5
0.55
0.6
softmax softplus sigmoid tanh selu
Activation function vs F score:
NoResult
NoResult
NoResult
0.48
0.5
0.52
0.54
0.56
0.58
0.6
Epoch-5 Epoch-10 Epoch-20 Epoch-30 Epoch-40 Epoch-50 Epoch-60 Epoch-70
F Score
Optimal Epoch Selection:
Conclusion & Future Work
12
❖ Conclusion
 Proposed a neural ensemble model for text suggestion detection
 Utilized the GRU architecture to learn better long-term dependencies
 Proposed model learned the contextual information effectively
 Experiments on benchmark datasets demonstrated the efficacy of our
method
❖ Future Work
 Leverage external knowledge to understand the review contents effectively
 Explore the problem of ambiguous, conversational, and extremely short
reviews
 Generalize our model for target-independent review detection within same
domain
 Evaluate our approach on other domain of interest

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一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
 

Suggestion Mining by Ahsan_CSE_CU

  • 1. University of Chittagong February 12, 2020 Md. Ahasan Ullah Department of Computer Science & Engineering A Gated Recurrent Neural Network-based Approach for Detecting Suggestions ID: 16701019 Supervisor Name: Dr. Abu Nowshed Chy
  • 2. Problem Statement 2 Determine whether a user review contains any informative suggestion Not Suggestion That hotel’s food was so delicious. Should have a juice glass in the roof Suggestion User Review Suggestion Detecting System
  • 3. Motivation 3 ❖ With the ever growing availability of opinions and reviews on the web, suggestion mining has become a popular research area ❖ Mining suggestions from huge review posts is beneficial to address several real-world problems
  • 4. Related Work 4  Domain adversarial neural networks for domain adaptation in suggestion mining (Mateusz et al., SemEval-2019)  Expert-informed pattern recognition for suggestion mining (Nelleke et al., SemEval-2019)  A stacked bi-lstm model for suggestion mining classification (Ding et al., SemEval-2019)  Suggestion mining using svm with handcrafted features (Ilia et al., SemEval-2019)  Mining suggestions from online reviews using deep learning techniques on augmented data (Rajalakshmi et al., SemEval-2019)  Exploring machine learning approaches in classifying text as suggestion or non-suggestion (Tirana et al., SemEval-2019)
  • 5. Methodology (Proposed Architecture) 5 L × D L = Text Length D = Word Vector Dimension Text Embeddings CNN
  • 6. Methodology 6 Convolutional Neural Network Architecture: Sentence Matrix Convolutional Layer Feature vector G R U Max-pulling layer Final class
  • 8. Methodology 8 Characteristics of GRU: ❖ Has two gates ▪ Reset ▪ Update ❖ No memory Unit Advantages of GRU: ❖ Works faster in less training data ❖ Easy to implement Characteristics of CNN: ❖ Has four layers ▪ Convolutional ▪ Pooling ▪ ReLU correction ▪ Fully-connected ❖ Works with filters Advantages of CNN: ❖ Can be used in both image and text ❖ A very good feature extractor
  • 9. Result 9 Dataset Description: ❖ SemEval 2019 Task 9 - Suggestion Mining from Online Reviews and Forums ❖ Dataset Statistics: ➢ Training Data: 8300 reviews ➢ Test Data: 833 reviews Performance with Various Filter Sizes:
  • 10. Result 10 Method F Score Li and Ding, 2019 (Ensemble Classifier, Attention-based- LSTM, TextCNN, C-LSTM, Bi-LSTM. Word2Vec) 0.6776 Our Proposed (2DCNN-GRU) 0.5806 Ding et al., 2019 (BiLSTM, LSTM. Word2Vec, GloVe) 0.5659 Markov and De la Clergerie, 2019 (SVM, Logistic Regression, Hand-crafted Features) 0.5118 Fatyanosa et al., 2019 (SVM, Linear Regression, Naive Bayes, CNN, GloVe ) 0.4730 Ahmed et al., 2019 (Containment Similarity, Maximum Common Subgraph, Tree-based Pipeline Optimization Tool) 0.3537 Comparative Performance on Suggestion Mining Dataset
  • 11. Result 11 0.4 0.45 0.5 0.55 0.6 softmax softplus sigmoid tanh selu Activation function vs F score: NoResult NoResult NoResult 0.48 0.5 0.52 0.54 0.56 0.58 0.6 Epoch-5 Epoch-10 Epoch-20 Epoch-30 Epoch-40 Epoch-50 Epoch-60 Epoch-70 F Score Optimal Epoch Selection:
  • 12. Conclusion & Future Work 12 ❖ Conclusion  Proposed a neural ensemble model for text suggestion detection  Utilized the GRU architecture to learn better long-term dependencies  Proposed model learned the contextual information effectively  Experiments on benchmark datasets demonstrated the efficacy of our method ❖ Future Work  Leverage external knowledge to understand the review contents effectively  Explore the problem of ambiguous, conversational, and extremely short reviews  Generalize our model for target-independent review detection within same domain  Evaluate our approach on other domain of interest