What is Deep Learning?

Just a Neural Network!

• "Deep learning" refers to Deep Neural Networks
• A Deep Neural Network is simply a Neural Network with multiple hidden layers
• Neural Networks have been around since the 1970s

So why now?

Large Networks are hard to train

• Vanishing gradients make backpropagation harder
• Overfitting becomes a serious issue
• So we settled (for the time being) with simpler, more useful variations of Neural Networks

Then, suddenly ...

• We realized we can stack these simpler Neural Networks, making them easier to train
• We derived more efficient parameter estimation and model regularization methods
• Also, Moore's law kicked in and GPU computation became viable

So what's the big deal?

MASSIVE improvements in Computer Vision

Speech Recognition

• Baidu (with Andrew Ng as their chief) has built a state-of-the-art speech recognition system with Deep Learning
• Their dataset: 7000 hours of conversation couple with background noise synthesis for a total of 100,000 hours
• They processed this through a massive GPU cluster

Cross Domain Representations

• What if you wanted to take an image and generate a description of it?
• The beauty of representation learning is it's ability to be distributed across tasks
• This is the real power of Neural Networks

But Samiur, what about NLP?

Deep Learning NLP

• Distributed word representations
• Dependency Parsing
• Sentiment Analysis
• And many others ...

Standard

• Bag of Words
  • A one-hot encoding
  • 20k to 50k dimensions
  • Can be improved by factoring in document frequency

Word embedding

• Neural Word embeddings
  • Uses a vector space that attempts to predict a word given a context window
  • 200-400 dimensions

motel [0.06, -0.01, 0.13, 0.07, -0.06, -0.04, 0, -0.04]

hotel [0.07, -0.03, 0.07, 0.06, -0.06, -0.03, 0.01, -0.05]

Word Representations

Word embeddings make semantic similarity and synonyms possible

Word embeddings have cool properties:

Dependency Parsing

Converting sentences to a dependency based grammar

Simplifying this to the verbs and it's agents is called Semantic Role Labeling

Sentiment Analysis

• Recursive Neural Networks
  • Can model tree structures very well
  • This makes it great for other NLP tasks too (such as parsing)

Get to the applications part already!

Tools

• Python
  • Theano/PyLearn2
  • Gensim (for word2vec)
  • nolearn (uses scikit-learn
• Java/Clojure/Scala
  • DeepLearning4j
  • neuralnetworks by Ivan Vasilev
• APIs
  • Alchemy API
  • Meta Mind

Problem: Funding Sentence Classifier

Build a binary classifier that is able to take any sentence from a news article and tell if it's about funding or not.

eg. "Mattermark is today announcing that it has raised a round of $6.5 million"

Word Vectors

• Used Gensim's Word2Vec implementation to train unsupervised word vectors on the UMBC Webbase Corpus (~100M documents, ~48GB of text)
• Then, iterated 20 times on text in news articles in the tech news domain (~1M documents, ~300MB of text)

Sentence Vectors

• How can you compose word vectors to make sentence vectors?
  • Use paragraph vector model proposed by Quoc Le
  • Feed into an RNN constructed by a dependency tree of the sentence
  • Use some heuristic function to combine the string of word vectors

What did we try?

• TF-IDF + Naive Bayes
• Word2Vec + Composition Methods
• Word2Vec + TF-IDF + Composition Methods
• Word2Vec + TF-IDF + Semantic Role Labeling (SRL) + Composition Methods

Composition Methods

Where wi represents the i'th word vector,

wv  the word vector for the verb, and a0 and a1 are agents

What worked?

• Word2Vec + TFIDF + SRL + Circular Convolution /Additive
  • The first method with simple TFIDF/Naive Bayes performed extremely poorly because of it's large dimensionality
  • Combining TFIDF with Word2Vec provided a small, but noticeable improvement
  • Adding SRL and a more sophisticated composition method increased performance by almost 5%

What else could we try?

• Can we apply this method to generate general purpose document vectors?
  • We are currently using LDA (a topic analysis method) or simple TFIDF  to create document vectors
  • How will this method compare to the already proposed paragraph vector method by Quoc Le?
• Can we associate these document vectors with much smaller query strings?
  • eg. Search for artificial intelligence against our companies and get better results than keyword search