Interactive Spoken Content Retrieval

Installation

1. Lasagne, Theano
2. Progressbar, tqdm
3. tsne, cprint

Work flow

1. Built language models from PTV transcripts * Transcript directory should be a directory with T0001,T0002,...T5047 transcription files * Specify transcript directory in src/transcript2docmodel.py * Takes approximately 6 hours mainly due to 100k (keyterm) * cmd: python src/transcript2docmodel.py
2. Train agent * run.py: Specify data, fold, feature, experiment_prefix(directory to save results), result_directory with argparser * Other argument can be adjusted/added/altered, see for yourself * cmd: python src/run.py
3. View Results * Use merge_csvs.py to merge result/*.log * cmd: python result/parse_log_to_csv.py $dir

Change feature

1. Change feature type: src/IR/statemachine.py, run_training.py - if/else condition in constructor, featureExtraction & argparser

Change cost

1. Change cost table: src/IR/actionmanager.py, possibly add another option in run_training.py, argparse

Visualize

1. specify network pickle ,feature file, number of features, save_path with src/run_visualize.py
2. use jupyter notebook to open result/plot_feature_action.ipynb & previous save h5 file

Other Notes

• Don't ask me about the code and the data storage format, it's just as it is
• I believe there are bugs in Wen's data, naming a few
  • Some keyterms/requests do not exist, can reproduce if I can access Wen's recognition transcripts
• Other cutting methods: snownlp