HG2051 (AY22/23) Project 1: Individual assignment

Introduction

This project constitutes 30% of your final grade for HG2051. Please work on the final program and report individually. Your code will be assessed based on its functionality and simplicity.

• The goal of this assignment is to demonstrate your programming and reasoning abilities by working on a problem individually. If you have an idea for another project that you would like to do instead, talk to me for approval.

• This project involves sorting, searching, and classifying lexical resources. Submission requirements include your data, output, and annotated code along with a short writeup describing your goals, process, and results.

Project 1: Multilingual search

While it is relatively easy to search through a corpus of text to find particular words/collocations/phrases in a single language, there are very few search tools that will allow you to find matches based on multiple languages. For this project you will develop a program that will return matches from multiple levels of an interlinear text.

1. Find a language with interlinear texts that have been glossed in English. You can find individual language resources such as for Pnar online, as well as aggregated data such as the ODIN project. Make sure you choose a language that has multiple texts available. Once you have chosen the language, select one of the linguistic texts for testing your tool.

2. Write a Python script that will read in the text, store it, and allow for a user to search the data on multiple levels simultaneously, i.e. text, gloss, part of speech (POS). The idea here is that the user should be able to query for all nouns that have the gloss dog, and look at all sentences with those forms in order to observe the context in which they occur. The implementation should enable searching using at least two levels of annotation (i.e. text, gloss), but will receive a higher score if more than two annotation levels are supported, as well as if forward and backward searches (i.e. “all words with POS n preceded by POS clitic”) are possible.

The search query can be implemented either through user interaction or via editing the script itself or some other method - whichever way you choose, this should be clearly documented. The script should also return data in some way, either by printing to the terminal or writing to a file.

3. Once your Python script is working for the first text you selected, test it on the second text to make sure that it works, and make any necessary changes so that it works for both texts. Finally, modify your program so that you can search across both texts simultaneously. Bonus: get it to work on multiple additional texts in the same format.

4. Write a short paper (no longer than 5 pages) describing your goals, data, process, and results. This paper should include some background information on the language you chose, with relevant linguistic information and citations for your sources. Submit this paper as a PDF along with your annotated/commented Python code and the texts you chose in a Github repository. The PDF should also be submitted via TurnItIn. Make sure to provide examples of your queries and results in the paper or as separate files, and describe any challenges you faced.