Homophone disambiguation-unigram-bigram model , Basic Statistics

The BBC hosts a homophone quiz on its website; your task for this lab is to develop an automatic method for completing the quiz, with the aim being to get as high a score as possible. Since this is the fourth lab, and you have no doubt become language processing experts, we have chosen the advanced quiz!

The questions are as follows (the blank indicates the position of the disputed word, and the words appearing in brackets at the end are the possible options).

1. I don't know to go out or not (weather/whether)

2. Houses were being built on this (site/sight)

3. We went the door to get inside (through/threw)

4. I really want a car (new/knew)

5. They all had a of the cake (piece/peace)

6. She had to go to prove she was innocent (caught/court)

7. We were only to visit at certain times (allowed/aloud)

8. We had to a car while we were on holiday (hire/higher)

9. Tip the jug and lots of cream on the strawberries (poor/pour/paw)

10. She went back to she had locked the door (check/cheque)

For the purposes of this exercise, you will use a simple language model to estimate the probability that each of the candidate words is correct. To do this you will need to compute the frequency of each of the words in a large corpus. Additionally you will need a count of all bigrams (two word sequences) in the corpus. Using nltk this becomes trivial. For this lab we will be using the entire Brown corpus to get these counts.

1 Unigram Model

The first part of this lab is to use a very simple model to select the word which goes in the blank: simply pick the most frequent word (using the unigram frequencies above). You should write a Python program to read in the sample sentences available.

Your program should then output for every sentence the candidate word it thinks should go in the blank.

2 Bigram Model

The second method you should attempt is to make use of the bigram counts to determine which of the potential candidates makes the whole sentence more probable (i.e. you should develop a basic language model). If one is willing to make certain assumptions, the probability of a sequence of words w1,w2,w3,. . .,wn is given by:

782_biagram model.png

When using a bigram language model, we approximate the above probability with using only the previous word:

1830_biagram model1.png

You should think about the entire calculation you need to make, and which parts of it are common to all possible choices in the blank space for the homophone disambiguation task.

We estimate the bigram probabilities in the equation above using counts from a large corpus.

The standard way to estimate bigram probabilities is:

1934_Biagram 3.png

3 Smoothing

Results for the task can be improved using smoothing. Implement the "Plus One Bi-gram

Smoothing" that was described in lecture. The bigram probabilities are estimated as:

1081_biagram 4.png

where V is the number of distinct words in the training corpus (i.e. the number of word types).

4 Hand-in

Hand in four files:

1. A Python program called lab4a.py that reads on standard input a file of sentences in the format of the test file supplied and outputs on standard output one word per line, where the word on the k-th line is that homophone from the pair of homophones at the end of the k-th input sentence which the unigram model (section 1 above) predicts as the most probable to fill in the blank in the k-th input sentence.

2. A Python program called lab4b.py which is the same as lab4a.py, except that the words proposed should be the homophones deemed most probable by the bigram model (section 2 above).

3. A Python program called lab4c.py which is the same as lab4b.py, except that the words proposed should be the homophones deemed most probable by the bigram model with plus-one smoothing (section 3 above).

4. A brief report (maximum 1 side of A4 - half a side is fine) called lab4-report (.doc or.pdf) that:

_ Describes how your programs work and reports the result for each.

_ Discusses why you get the results you get.

Posted Date: 3/1/2013 5:57:49 AM | Location : United States

Related Discussions:- Homophone disambiguation-unigram-bigram model , Assignment Help, Ask Question on Homophone disambiguation-unigram-bigram model , Get Answer, Expert's Help, Homophone disambiguation-unigram-bigram model Discussions

Write discussion on Homophone disambiguation-unigram-bigram model
Your posts are moderated
Related Questions
Shafts are cut to length by two machines, A and B. Each machine cuts 50 percent of all shafts in approximately the same amount of time. Machine A's shafts are all good, but Machine

Agglomerative hierarchical clustering methods/procedures Methods of cluster analysis that start with each individual in the separate cluster and then, in the series of steps, c

1. (i) Complaints were made about the level of pollutants in the discharge from a certain factory.  The factory refuted the complaints by showing the results of their own analysis

What are the assumptions of the technique?

'Statistics is the backbone of decision-making'. Comment.

SCENARIO It is proposed to construct a 3m diameter, 50m long tunnel, heading 0950, through the andesite lava flow at Maori Bay (Muriwai). As part of the preliminary investigati

explain different types of assets..

Define Payroll Withholdings The U. S. earnings tax system-as well as most condition earnings tax systems-requires business employers to hold paycheck taxation from their staff'

X 420 610 625 500 400 550 650 480 565 Y 2.80 3.60 3.75 3.00 2.50 2.70 3.50 3.90 2.0 2.95 3.30 Calculate te covarience anf the correlatio coeficient. Comment on the relationship be