adapted from Jorge Moraleda and Jurafsky & Martin (2008)

Turning it in: You’ll turn in everything described below as a zip file via Canvas.

## Regular expressions

In this assignment you will write a program that will take as input one plain text file and will print as output every sentence of the input file that contains a time point. Finding time points is a frequent task in text analytics. Regular expressions are well suited for this task and that is what we will use for this assignment.

A time point could be an absolute date (e.g., “October 31st, 2013” or “10/31/13”), time (e.g., “14:00 PST”), season (e.g., Spring), etc., or a relative time (e.g., “last weekend”, “now”).

The examples above exemplify that in this task, as is often the case in text analytics, problems are not well defined at the onset. In this case, what is or not a time point will depend on the actual problem one is trying to solve. For this assignment you will have to decide what patterns you want your program to capture.

### Relevant packages

There are two components to this task. One is to split the text file into sentences and the other is to determine which sentences contain a time point using regular expressions.

Sentence splitting To split the file into sentences, you should use Stanford CoreNLP, to become familiar with the package, since we will be using it for later assignments. CoreNLP is Java software, so you may use it natively from Java. To access it from within Python, you’ll need to use a wrapper. I recommend the one, which requires that you’re running the corenlp server.

If you’re using the wrapper, note that splitting text into sentences the default way will also tokenize the text, meaning you’ll need to stitch those tokens back together to recover the full sentence string so you can run it through your regular expressions.

Regular expressions

Java and Python both have good built-in regular expression support: the re module in Python and java.util.regex in Java. In both languages, regular expressions are constructed from strings. You can assemble a very large string by concatenating smaller strings using the + operator. Assembling your final regular expression string from shorter substrings will allow you to give meaningful names to each substring and reuse them.

### Deliverables

1. A folder named src containing all your source code.

2. A plain text file classbios_timepoints.txt containing the output of your program when run with classbios.txt as input, one sentence per line.

3. A plain text file readme.txt containing an explanation of which patterns you have chosen or not chosen to capture, and any other thoughts you would like to share.

4. A plain text file regex.txt containing the regular expression that you have chosen to use to detect time points. If you used substrings, include the substrings and the logic to assemble them instead of the final string itself. It will help with understanding what you’ve done!

## Finite-state automata

1. For the above non-deterministic finite-state automaton, list the shortest 10 strings that the automaton would accept in a file called fsa.txt, one string per line, ordered by length starting with the shortest.