This assignment counts for 5% of the course grade.
Assignments turned in after the deadline but before Thursday, September 17 are subject to a 20% grade penalty.
In this assignment you will write a very simple lemmatizer, which learns a lemmatization function from an annotated corpus. The function is so basic I wouldn’t even consider it machine learning: it’s basically just a big lookup table, which maps every word form attested in the training data to the most common lemma associated with that form. At test time, the program checks if a form is in the lookup table, and if so, it gives the associated lemma; if the form is not in the lookup table, it gives the form itself as the lemma (identity mapping).
The program performs training and testing in one run: it reads the training data, learns the lookup table and keeps it in memory, then reads the test data, runs the testing, and reports the results. The program output is in a fixed format, reporting 15 counters and 5 performance measures. The assignment will be graded based on the correctness of these measures.
A set of training and test data is available as a compressed ZIP archive on Blackboard. The uncompressed archive contains the following files:
The submission script will learn the model from the training data, test it on the test data, output the results, and compare them to the known solution. The grading script will do the same, but on training and test data from a different language.
You will write a program called lookup-lemmatizer.py
in Python 3 (Python 2 has been deprecated),
which will take the paths to the training and test data files as
command-line arguments.
Your program will be invoked in the following way:
> python lookup-lemmatizer.py /path/to/train/data /path/to/test/data
The program will read the training data, learn a lookup table, run
the lemmatizer on the test data, and write its report to a file called
lookup-output.txt. The report has a fixed format of
22 lines which looks as follows:
Training statistics
Wordform types: 16879
Wordform tokens: 281057
Unambiguous types: 16465
Unambiguous tokens: 196204
Ambiguous types: 414
Ambiguous tokens: 84853
Ambiguous most common tokens: 75667
Identity tokens: 201485
Expected lookup accuracy: 0.967316238343
Expected identity accuracy: 0.716883052192
Test results
Total test items: 35430
Found in lookup table: 33849
Lookup match: 32596
Lookup mismatch: 1253
Not found in lookup table: 1581
Identity match: 1227
Identity mismatch: 354
Lookup accuracy: 0.962982658276
Identity accuracy: 0.776091081594
Overall accuracy: 0.954642957945
The numbers above are a correct output when running the program on the supplied data (the submission script). There is some variability possible in the output; see note below. The numbers will be different when the program is run on the data used by the grading script.
Starter code is available on Blackboard, which already reads the input and formats the output correctly. You are strongly encouraged to use this starter code; you will need to write the code that performs the counts and calculates the results correctly.
All submissions will be completed through Vocareum; please consult the instructions for how to use Vocareum.
Multiple submissions are allowed; only the final submission will be
graded. Each time you submit, a submission script is invoked, which
runs the program on the training and test data.
Do not include the data in your submission: the submission script
reads the data from a central directory, not from your
personal directory.
You should only upload your program file to Vocareum, that is
lookup-lemmatizer.py;
if your program uses auxiliary files then you must also include these
in your personal directory, though for this exercise there is probably
no need for auxiliary files.
You are encouraged to submit early and often in order to iron out any problems, especially issues with the format of the final output.
The output of your lemmatizer will be graded automatically; failure to format your output correctly may result in very low scores, which will not be changed.
For full credit, make sure to submit your assignment well before the deadline. The time of submission recorded by the system is the time used for determining late penalties. If your submission is received late, whatever the reason (including equipment failure and network latencies or outages), it will incur a late penalty.
After the due date, we will run your lemmatizer on training and test data from a different language, and compare the output of your program to a reference output for that language. Each of the 20 numbers calculated will count for 5% of the grade for the assignment.
Ties in the training data. The problem definition does not state what to do in case of ties in the training data, that is when for an ambiguous word form, there are two or more most common lemmas. In this case, either of the lemmas could enter the lookup table, which could cause a small amount of variation in the test data. This is expected, and the grading script will allow for this variation; for the supplied data (the submission script), the following is legitimate (correct) variation: