Tracking

Stefan

01.11.

• Read about neural language models in
  • A Neural Probabilistic Language Model
    • Learning simultaneously: Distributed Word Vector Representations and a Statistical Language Model (given all previous words, what's the probability distribution for the next word?)
    • Shallow model, but can process large datasets
    • N previous Words are translated via 1-V-Mapping
  • Efficient Estimation of Word Representations in Vector Space
  • Read https://code.google.com/p/word2vec/

02.11.

• Read through distance.c in word2vec to understand word2vec binary format
• Write Python program to read word vectors with Joseph
• Set up virtual python environment on server

09.11.

• Basic network, and experiment infrastructure
• Write glue code connecting sentence files, sliding window and level db creation

12.11.

• Create databases, run first experiment on server
• Caffe Multi-class Precision and Recall

Joseph

• Read https://code.google.com/p/word2vec/
• Understand distance.c, implement Python program to read word vectors
• Explore how to use NLTK for POS tagging
• Write python script for demo show cases
• Documentation of demo script
• Net configuration script

Tanja

• Read https://code.google.com/p/word2vec/
• Raad some papers
• Write Python script to parse xml and ASR transcript files
• Write Python script to create basic training instances using a sliding window
• Write training instances to leveldb script
• Ensure valid train and test split
• Caffe Multi-class Precision and Recall
• Pipeline work
• Use POS-Tags as features

Ricarda

• Read https://code.google.com/p/word2vec/
• Read papers to get familiar with Deep Learning
• Write Python script to parse xml and ASR transcript files
• Write Python script to create basic training instances using a sliding window
• Refactor script for creating trainings instances and work on pipeline to create instances
• Write python script for demo show cases