Use the working script: new_data_pipeline.sh. Basically, it does the above.

Or do the following step by step by hand(not recommended)

1. python print_filenames_and_titles.py: get the file paths and news titles that accords to our requirement(non-monocase title and non-empty article body)
2. python copy_puls_file_to_local.py: copy the files somewhere writable&accessible
3. python extract_doc_ids.py: save the ids of documents to be used
4. puls-core-process-document.sh: using PULS to preprocess the documents. This will generate the .auxil files
5. process_and_save_capitalized_headlines.py: save the malformed headlines somewhere
6. make_data_puls.py: extract the features for CRF classifier to use
7. train_puls_model.sh: train the model

The process is divided into two parts: one part is shared with the data creation process for CRF classifier(step 1 to 5).

The other is outputing the labels in separate files for the rule-based classifier to use.

Run make_rule_based_corpus.sh

Refer to the comments in train_puls_model.sh and comment/uncomment certain lines to do that.

Itermediate performance statitics(as they will be processed later) will be saved in target paths as specified in that script.

Do the following:

• Change the variables in puls-rule-based-parallel.sh if you'd like to
• Run puls-rule-based-parallel.sh to use the IE rule-based capitalization recovery tool to process the evaluation data
• Go to the directory specified by $result_dir variable in the puls-rule-based-parallel.sh and concatenate all the result files (starting with id_) into a whole result file
• Run python evaluate.py to print the result matrix, where rows are the statistics for each label and columns are number of match, number of predictions y model and number of lables in reality

short-cut:

As the data preparation is done, if you want to evaluate rule-based classifier, just run the above plus the final post score processing.

Both evaluation scripts print out itermediate result(like number of correct predictions support) for the final scores. You need to run replace the data in calc_cv_result.py according to the comment in the script and run it.

Documents are filtered by whether their title is trainable(correctly-capitalized) and whether they contain non-empty body, the list of document ids is saved under data/tmp/2015-08-18/filtered_trainable_doc_ids.txt

For CRF classiier, pred_err.py will print out the error examples as well as confusion matrix

> # Example: python pred_err.py ${model_path} ${test_sentence_path} ${test_sentence_feature_path}
> DATA_ROOT=/cs/taatto/home/hxiao/capitalization-recovery
> python pred_err.py --model ${DATA_ROOT}/result/feature/cap/1+2+3+4+5+6/model --sent_path ${DATA_ROOT}/corpus/news_title_cap/30000/test.txt --crfsuite_path ${DATA_ROOT}/result/feature/cap/1+2+3+4+5+6/test.crfsuite.txt

For rule-based classifier, evaluate.py will do the same role. Note, you need to set print_errors=True when calling eval_rule_based in the evaluate.py script.

• Add more features to handle mixed-case words, for example: TSX-Venture, or split the word by the hyphen
• In capitalized titles(more information is preserved), some words are already all-uppercase/mixed-cased. Dictionary feature does not take into account mixed case words.
• Spelling/morphology, funds = fund + s
• POS tag for capitalized words seems to tend to be NNP. Maybe lowercase the sentence and capitalize it?