ask-wooster. Not as smart as asking Jeeves, but we'll give it an honest go!

The project uses the GNU-Make commands for housekeeping. Please have both GNU Make and Python 2.7 installed and available in the system PATH. Additionally, please have the virtualenv python package installed.

1. Unzip the documents into the resources/doc_dev folder
2. make ask-wooster and make check-wooster for dev-set
3. make test-wooster for test-set
4. Note that make preprocess and make preprocess-test must be done before the corresponding answering steps.

1. Preprocess all answers in corpus to POS tag and NER-class augmented list of sentences
2. Read questions file into memory and parse into tokens
3. For each question, read the entire 100-document folder into memory and parse into a list of paragraphs (which in turn have sentences/tokens) with feature annotations from preprocessing
4. Extract candidate answers from window around tokens with the NER-type we're looking for
5. Compute the ranking-score of the candidate answers
6. For the top 5 answers, create an answer object consisting of the question id, text, and answer-document id
7. Write out the ranked list of answers in the format specified in the instructions, to provided answer file

• Removes function words from corpus when considering relevance
• Perform Named Entity Recognition (NER) to extract intelligent guesses of answers
• Additional custom NER-like detection of time-types

• Got the list from http://www.myenglishpages.com/site_php_files/vocabulary-lesson-function-words.php
• Meaning of Penn-Treebank POS from https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html