Description
Question 1: POS Tagging (20 points)
Train a first-order (i.e., the probability of a tag depends only on the previous tag) HMM part-of-speech
tagger by hand on the following training corpus. Find the MAP estimate of the parameters of the model
using add-1 smoothing.
That/C that/N is/V , is/V .
That/C that/N is/V not/N , is/V not/N .
Is/V that/N it/N ?
Is/V it/N that/N good/J ?
Ignore capitalization differences and punctuation. They are only there for readability purposes. There
should thus be a tagset of size 4, and a lexicon of size 5. Your model should contain the following parameters: Π (initial state probabilities), A (state transition probabilities), and B (emission probabilities).
Next, run the Viterbi algorithm (by hand) to obtain a POS tagging for the following test sentence. Show
the trellis cell values and your calculations.
Not that good .
Question 2: Grammar for French (40 points)
In this question, you will develop a context-free grammar for a fragment of French. Your grammar must
account for various aspects of the French language, as listed below.
Basic sentence word order in the present
The basic word order in French is Subject-Verb-Object, as in English:
(1) Je regarde la t´el´evision.
I watch the television
(2) Le chat mange le poisson.
The cat eats the fish
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Subject-verb agreement
Just as in English, the subject must agree with the verb in number and person:
(3) Tu regardes la t´el´evision.
You(2Sg) watch the television
(4) Il regarde la t´el´evision.
He watches the television
(5) Nous regardons la t´el´evision.
We watch the television
(6) Vous regardez la t´el´evision.
You(2Pl) watch the television
(7) Ils regardent la t´el´evision.
They(Masc.) watch the television
Look up the list of subject pronouns in French, as well as the verb conjugation paradigm for several
common verbs using an online website. Include these in your grammar.
Reference: http://www.wordreference.com/conj/FrVerbs.aspx
Definite noun phrases and proper names
A definite noun phrase in French follows a similar order as in English (article + noun). However,
the article must agree with the noun in number and grammatical gender. Grammatical gender is a
more-or-less arbitrary categorization of nouns into either masculine or feminine.
Examples:
(8) Le chat
the(Masc.) cat
(9) La t´el´evision
the(Fem.) television
(10) Les chats
the(Pl.) cats
(11) Les t´el´evisions
the(Pl) televisions
As you can see, there is no distinction in the plural between masculine or feminine.
Some proper names in French do not take articles, just as in English:
(12) Jackie
Jackie
(13) Montr´eal
Montreal
Others do (e.g., le Canada), but you do not have to handle them.
Direct object pronouns
When a pronoun is a direct object of the verb, they precede the verb:
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(14) Il la regarde.
He it(Fem.) watches.
Look up the list of direct object pronouns in French, and enhance your grammar to account for the word
order with direct objects.
Attributive adjectives
Adjectives typically follow the noun that they modify in a noun phrase:
(15) Le chat noir
the(Masc.) cat black
(16) Le chat heureux
the(Masc.) cat happy
However, other adjectives precede the noun:
(17) Le beau chat
the(Masc.) beautiful cat
(18) Le joli chat
the(Masc.) pretty cat
Yet others may precede OR follow the noun, though the meaning usually changes slightly:
(19) La derni`ere semaine
the(Fem.) last week
the last week (e.g., of the year)
(20) La semaine derni`ere
the(Fem.) week last
last week (i.e., the one before this week)
In addition, adjectives must agree with the noun that they modify in number and gender:
(21) Les chats noirs
the(Pl.) cats black(Pl.)
the black cats
(22) La t´el´evision noire
the(Fem.) television black(Fem.)
the black television
(23) Les t´el´evisions noires
the(Pl.) televisions black(Fem. Pl.)
the black televisions
Note that adjectives do distinguish masculine from feminine in the plural.
Find several adjectives of each of the three classes above, and incorporate them into your grammar.
References
http://french.about.com/od/grammar/a/adjectives.htm
http://french.about.com/od/grammar/a/adjectives_4.htm
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Examples and submission format
You already have many examples that your grammar should accept (though many of the above examples
were only noun phrases, not full sentences). Here are some sentences that your grammar should reject:
(24) *Je mangent le poisson.
(25) *Les noirs chats mangent le poisson.
(26) *La poisson mangent les chats.
(27) *Je mange les.
Use the following nonterminals to indicate grammatical categories:
S sentence/clause
NP noun phrase
VP verb phrase
N noun
PN proper noun
PR pronoun
V verb
DT determiner
A adjective
You may add further non-terminal categories or subdivide them (e.g., V-1Sing) as needed. Don’t forget
the lexical rules! Include enough lexical items such that each of the syntactic categories can be expressed
in at least three different ways.
Write your grammar in a text editor using a predictable, computer-readable format. For instance, here
is one possible rule:
S -> NP VP
Here is another example of a set of four rules (here, they are lexical rules):
V-1Sg -> mange | aime | regarde | cherche
These are just examples, and are not necessarily the rules you want in your grammar! Ignore punctuation
and capitalization in your grammar (just use all lower-case, except for proper names). French has
contractions in many cases where a word begins with a vowel (e.g.,j’aime rather than *je aime). You
may ignore such issues.
Submit your grammar as a plaintext .txt file. Show instances where your grammar correctly accepts and
rejects some sentence. In addition, answer the following questions in your response to the question:
1. What are some advantages of modelling French grammar with a CFG, compared to using an FSA?
2. What are some disadvantages of modelling French grammar with a CFG?
3. What are some aspects of French grammar that your CFG does not handle?
This question is rather open-ended; your grammar will be judged on the following points:
• Whether you followed the specifications above (e.g. names of non-terminals, minimum number of
lexical entries)
• Coverage of the required grammatical constructions
• Clarity of the grammar
• The responses to the questions above
You won’t get extra points for having many additional lexical items that exhibit the same type of
behaviour!
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Question 3: Decipherment with HMMs (40 points)
We have intercepted coded communications from our enemies, the League Against Natural Language
Processing. We have obtained short samples containing both ciphertext (i.e., encrypted text) and its
associated plaintext (i.e., plain English). Being good experimentalists, we have separated the data into
a training set and a test set, so that we know that we will be able to decrypt future coded messages
from this despicable organization.
Actually, we have intercepted three different ciphers of the sample text:
1. The first cipher is quite archaic, first used by Julius Caesar. Each letter in the plain text is shifted
by a fixed amount to produce the cipher text.
2. The second cipher is a more complex cipher, in which there are two letter substitution ciphers. When
encrypting each letter, one of the two is randomly chosen, and that cipher is used to generate the
ciphertext letter.
3. The third cipher was invented by Gilbert Vernam! The cipher text is produced by adding integer
values of the a key and the plain text. We also know that the key is produced by shifting characters
in plain text by 3 places from right to left. For example if you need to encrypt the plain text
‘nlp is awesome.’ the key you will use is ‘ is awesome.nlp’. To generate the cipher you need
to add the integer values of the two strings character by character. Integer values for characters
from a-z is 0-25 and for ‘ ’, ‘,’, ‘.’ are ‘26’, ‘27’ and ‘28’ respectively. Thus the cipher text will be
‘ktexilbshqwnzpo’.
Note: The three cipher texts are available on the course website
The plaintext and ciphertext alphabets are both the 26 letters of the alphabet (lowercase), plus the
space, comma, and period, for a total of 29 symbols.
In this question, we will explore several HMM models for solving these ciphers. Each HMM sample will
be a sentence, and each time step within a sequence will be one character. The hidden layer will consist
of the plaintext characters, while the observed layer will consist of ciphertext characters.
Standard HMM
Implement a system which trains a standard HMM on the training set using MLE, and tests on the
testing data. Use NLTK’s nltk.tag.hmm. Report per-token accuracy on both ciphers. Print out the
results of decoding on the test set. What do you observe about the performance of the model?
Your code should run in the following way:
python decipher.py
It should print out the deciphered test set, and report the accuracy score. Since training should not take
that much time, you do not need to save the model output.
Laplace smoothing
Let’s see if smoothing will help improve performance. Modify your code and add an option that implements Laplace smoothing during training. The simplest method for doing so will likely be to modify the
HiddenMarkovModelTagger object that you got from training from the previous step. Consult NLTK’s
API in order to figure out how to do this. You may find the page on nltk.probability useful as well.
It should be possible to turn Laplace smoothing on at the command line in the following way:
python decipher.py -laplace
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Improved plaintext modelling
The training set that we have in this question is very small. Maybe we can further improve performance
by having a better model of character bigram transitions in English. Change your training procedure to
incorporate this information by preprocessing and getting character transition counts from the samples
of English you have from Assignment 1. These counts should supplement, not replace the counts that
you get from the original training set. You will have to deal with the following issues:
1. Sentence segmentation
2. Lower-casing the text
3. Removing any other character which is not one of the 29 symbols
4. Removing extra space from the beginning and end of each sentence
How does this change affect the results?
It should be possible to turn this option on at the command line in the following way:
python decipher.py -lm
For this step, you should save and submit either the preprocessed A1 corpus or your counts thereof,
so that the TA can reproduce your results. In addition, it should be possible to turn on both Laplace
smoothing and the improved language modelling.
Report and submission requirements
Experiment on the three ciphers, reporting accuracy for each of the settings in a table. Write a brief (max.
half-page) report on the results, noting whether each change was successful in improving performance.
Were there any surprises or unexpected results? Do you achieve close to perfect accuracy? If not, why
not? Try to explain and speculate on the reasons for these results.
What To Submit
Your submission must be made through MyCourses, and must consist of the following three files:
1. The written portion of Questions 1, 2 (except the grammar), and 3 as a .pdf file called ‘a2-
written.pdf’.
2. The grammar file of Question 2, as a plaintext .txt file called ‘french-grammar.txt’.
3. For the programming part of Question 3, you should submit your source code and any other files
necessary to regenerate your results as described above as a single .zip file called ‘a2-q3.zip’.
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