README.md

SequenceFramework

Process & Select Important Sequence.

Select important text/words in sentence sequences(documents).

By proposing a new NLP sequence processing deep learning framework.

(Implement by TF2.0)

Models/data: https://drive.google.com/drive/u/1/folders/1DOpFaLUaYyzTBgLqVcjgUwfI33m5frQK

• one-hot encoding training.csv (A tri-gram is represented by 1377-dim vectors. Each profile has 182 tri-grams, including padding. Last column is the label.)
• one-hot encoding testing.csv

20 newsgroups Dataset

• The 20 newsgroups text dataset: http://qwone.com/~jason/20Newsgroups/
• 20news-bydate.tar.gz - 20 Newsgroups sorted by date; duplicates and some headers removed (18846 documents)

Reuters dataset (baseline model)

• Reuters dataset in Keras https://keras.io/datasets/ 、 https://keras.io/examples/reuters_mlp/ 、 https://github.com/nltk/nltk_data/tree/gh-pages/packages/corpora

Dataset Length Distribution:

Statistics	Value
mean	145.964197
std	145.878476
mode	17.000000
min	2.000000
q1	60.000000
median	95.000000
q3	180.000000
max	2376.000000
iqr	120.000000
outlier	360.000000
far_out	540.000000
10%	35.000000
20%	53.000000
30%	67.000000
40%	81.000000
50%	95.000000
60%	112.000000
70%	154.000000
80%	206.000000
90%	315.000000
100%	2376.000000

• (diagram in results/: length_dist_diagram.xlsx)

• pick max length = oulier = 360

• all words #: 30979

  • words frequency diagram in results/: words_dist_diagram.xlsx
  • pick 8352 words (count>=10)

• 80% for training: 8260 train sequences

• 20% for testing: 2066 test sequences

Preprocessing REF

• https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html

Framework

• MTL(2 optimizers)?weighted loss(1 optmizer)?
• end2end

Hyper-parameters & Experiments

• max_words、max_length
• hidden_dim
• init_w、init_b
• activation function slope: portion
• model1 arch.、model2 arch.
• batch_size
• loss seq: seq_num
• optimizer
• weighted loss: alpha、beta、gamma
• input weight or binary: pred_imp or pred_imp2
• Experiments parameters detail link: https://docs.google.com/spreadsheets/d/1iSLUaFhr27HRi2YB8bPCeuOCmBx18nkiCAKWk4Fg8JA/edit#gid=0

model1: Embedder + (Encoder) + Filter

• Input: ID of words (int)
• Output: embedding vectors(float32, tensors) & filter(float32, tensors)
  • round filter to binary number (0 or 1) => filter2
• loss1(alpha): mean of filter
• loss3(gamma): consecutive of filter2
• ones_num: numbers of 1's portion

model2: Classifier

• Input: embedding vectors & filter2
• Output: classID
• loss2(beta): categorical cross entropy of classID
• acc_rate: accuracy in classification

Functions, Records & Statistics

losses_metrics.xlsx

• Train / Test family classify loss of each epoch
• Train / Test filter loss of each epoch、可調整希望模型1越多或是0越多、可調整已weight輸入或以binary輸入
• Train / Test sequence loss of each epoch、可調整連續之windows size (越低代表越連續)
• Train / Test accuracy of each epoch
• Train / Test 1's number of each epoch
• Train / Test each class' average intra-cosine-similarity of embedding vectors of each epoch
• Train / Test each class' average intra-cosine-similarity after Filter module of each epoch
• Train / Test inter-classes' cosine-similarity of embedding vectors of each epoch
• Train / Test inter-classes' cosine-simlarity after Filter module of each epoch

caseStudy_result.xlsx

• Original input text of each testing data in each epoch
• After Filter module's text of each testing data in each epoch
• True label of each testing data in each epoch
• Predicted label of each testing data in each epoch

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Below Deprecated

Possible solutions:

Module1: Embedding

• Possible method: BERT / learnable layer...

Module2: Filtering

• loss: sum of weights/scores
• Possible method: self-attention/LSTM/GRU
• activation function: gumble softmax/hard_sigmoid
• optimizer: Adam/Nadam/RMSprop
• Output: only binary(by rounded) / weight score

Module3: Classifier

• input: multiply of Module2 output and Module1 output
• loss: categorical cross entropy loss
• Possible method: self-attention/LSTM/GRU/CNN
• optimizer: Adam/Nadam/RMSprop