Description
You will write two programs in Python 3 (Python 2 has been deprecated): `hmmlearn.py` will learn a hidden Markov model from the training data, and `hmmdecode.py` will use the model to tag new data.
The learning program will be invoked in the following way:
> python hmmlearn.py /path/to/input
The argument is a single file containing the training data; the program will learn a hidden Markov model, and write the model parameters to a file called `hmmmodel.txt`. The format of the model is up to you, but it should follow the following guidelines:
– The model file should contain sufficient information for `hmmdecode.py` to successfully tag new data.
– The model file should be human-readable, so that model parameters can be easily understood by visual inspection of the file.
The tagging program will be invoked in the following way:
> python hmmdecode.py /path/to/input
The argument is a single file containing the test data; the program will read the parameters of a hidden Markov model from the file `hmmmodel.txt`, tag each word in the test data, and write the results to a text file called `hmmoutput.txt` in the same format as the training data.
The accuracy of your tagger is determined by a scoring script which compares the output of your tagger to a reference tagged text. Note that the tagged output file `hmmoutput.txt` must match line for line and word for word with the input to `hmmdecode.py`. A discrepancy in the number of lines or in the number of words on corresponding lines will cause the scoring script to fail.
Things to consider
– **Tags.** Each language has a different tagset; the surprise language will have some tags that do not exist in the Italian and Japanese data. You must therefore build your tag sets from the training data, and not rely on a precompiled list of tags.
– **Slash character.** The slash character `/` is the separator between words and tags, but it also appears within words in the text, so be very careful when separating words from tags. *Slashes never appear in the tags, so the separator is always the last slash in the word/tag sequence.*
– **Smoothing and unseen words and transitions.** You should implement some method to handle unknown vocabulary and unseen transitions in the test data, otherwise your programs won’t work.
– **Unseen words:** The test data may contain words that have never been encountered in the training data: these will have an *emission probability* of zero for all tags.
– **Unseen transitions:** The test data may contain two adjacent unambiguous words (that is, words that can only have one part-of-speech tag), but the transition between these tags was never seen in the training data, so it has a probability of zero; in this case the Viterbi algorithm will have no way to proceed.
The reference solution will use *add-one smoothing* on the *transition probabilities* and *no smoothing* on the *emission probabilities*. For unknown tokens in the test data, it will ignore the emission probabilities and use the transition probabilities alone, and also limit the tag inventory to only the tags with a large associated vocabulary (open-class items). You may use more sophisticated methods which you implement yourselves.
– **End state.** You may choose to implement the algorithm with transitions ending at the last word of a sentence (as in Jurafsky and Martin, figure 8.10), or by adding a special end state after the last word (see for example an older draft of Jurafsky and Martin, figure 9.11). The reference solution will use an end state.
– **Runtime efficiency.** Vocareum imposes a limit on running times, and if a program takes too long, Vocareum will kill the process. Your program therefore needs to run efficiently. One common source of runtime inefficiency in Viterbi decoding is multiplying a lot of incoming transitions by an emission probability of zero; testing for that zero and skipping all that multiplication can cut the runtime by 90%. Run times for the reference solution are *approximately 1 second* for running hmmlearn.py on the training data and *3 seconds* for running hmmdecode.py on the development data, running on a MacBook Pro from 2016.
