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Word vectorization(embedding) with nnlm

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hyunsung lee

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Engineering
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Word Vectorization(Embedding) with NNLM Intern 이현성 SungKyunKwan University, Data mining Lab
Contents • Brief intro to Keras • Backgrounds : simple linear algebra • Model description • Discussion • Go further
Brief intro to Keras
What is it? • Deep learning library(wrapper) for Theano and Tensorflow. • High-level Neural Network API
Example : Multilayer perceptron
Example : Convolutional neural network
If you want to know deeper • https://keras.io • https://keras.io/getting-started/sequential-model- guide/#examples
Backgrounds : simple linear algebra
Backgrounds
Backgrounds
Then, for what happen to the one-hot encoded vectors?
So we can use C’s row-vector for dense vector representation for word.(embedding) • How to implement it? • Is it work well?
Model description
Dataset Description • Corpus of Contemporary American English http://corpus.byu.edu/coca/ • 1 million most frequent 5-gram in the total corpus • No stemming or lemmatization done • Approximately 25000 words
Example of Dataset preprocessed by me W0 W1 W2 W3 W4 Both men and women reported i wanted something that was the hospital when he was to have a baby that policies of the clinton administrati on
Model architecture • Goal : Similar word have similar vector representation. • Input : N-gram word list • Output : list of probability’s of word t is word i
Model description CW0 CW1 CW2 CW3 W0 W1 W2 W3 CW0 + CW1 + CW2 + CW3 NNC를 곱해준다 Flatten W4 W4_hat 이 둘의 차이(negative log likelihood, log categorical cross-entropy)로 back- propagation Four vectors with dimension 30 One vector with dimension 120 One vector with dimension 120 Relu Softmax
How loss is calculated • V ={“미쿠가 오늘도 너무 귀여 워”} • vector representation :
Model description • # samples = 1 Million • Minibatch with epoch 1000 • # Iteration 50
Implementation with keras
Implementation with keras
Implementation with Keras
Discussion
This Vector representation actually is ‘vector representation’? • Similar vectors have similar meaning(in syntactic, semantic)?
Result. • find similar vectors with Trained feature vector Ci • KNN with Euclidean metric used word 1st 2nd 3rd 4th 5th Look Looks Looking Stared Peek glance Run Ran Running term Pass Runs Talk Talked Talking Story Bones Truth Know Guess Thinking Knowing Knows sure Boy Girl Woman Man Africa Doctor Year Week Weeks Days Decade Month Times Moment Day Nights Night Pause
results • 잘 안 된 것들…? word 1st 2nd 3rd 4th 5th The Our United Your White Main Japan Russia Slavery Terrorism Britain Sector Indian Competitive Humanitarian Regulatory Canadian Investigative New His Our Its My your Your Our My His White Their Gay Missile Reproductive Governmental Preventive Same-sex A Presidential San Foreign The domestic
Discussion • Good syntactic similarity for most words. • Good semantic(meaning) similarity for nouns and verbs • Bad semantic similarity for other words(adjectives, or etc…) • I think this is mainly because I skipped • lemmatization(erasing unimportant words such as ‘a’, ‘the’.......) • stemming (hashing words like ‘did’, ‘do’ and ‘done’ into single ‘do’)
Go further(다음 발표 때 할 거) • Use Skip-gram or CBOW • toward better word to vector representation • Better efficiency • Larger corpus size • Visualization for word models
Use Skip-gram or CBOW
Proper visualization for word models
실제로는 하나도 안 닮음…;;;

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