[SOLVED] LM Data Mining and Machine Learning 2024 Lab 1 Text Retrieval Matlab

LM Data Mining and Machine Learning (2024)

Lab 1 – Text Retrieval

PART 1: TF–IDF BASED TEXT RETRIEVAL

Objective

The objective of this lab session is to apply the text-based Information Retrieval (IR) techniques which we have studied in lectures, namely:

1. Stop word removal

2. Stemming

3. Construction of the index – calculation of TF-IDF weights

4. Retrieval – calculating the similarity between a query and document

We will apply these techniques to a ‘toy’ corpus consisting of 112 documents – BEng final year project specifications.  These project specifications were submitted by staff in Word format, but I have converted them all into plain text files for the purposes of this lab.   However,  I  did  not remove the formatting or the pieces of text which are common to all of the files.

Copy the zip archive lab1-2024from Canvas and ‘unzip’ it.  You should end up with a new folder called lab1-2024 containing all of the files that you need to complete the lab, including a folder called docOrig which contains 112 text files.

The folder lab1-2024 will be the default folder that you work from.  Have a look at one of the text files in the docOrig folder. You should be able to identify the common formatting.

Processing of the documents

Before we can do IR we need to apply stop word removal and stemming to each of the documents in our corpus.  To do this you will use two executable (.exe) files of the C programmes that are in your  lab1-2024 folder:  stop.exe and porter-stemmer.exe.   Note  that  there are also source C programmes provided in a case your computer runs on a non-Windows operating system –  in  that   case,  you  will   need  to   compile  the   source  C   programmes  (stop.c,  porter- stemmer.c, index.c and retrieve.c).

Task 1: Stop word removal: The next task is to remove stop words from each of the documents. The 50-word stop word list stopList50 should already be in your lab1-2024folder. Now run the program stop on one of the documents – AbassiM.txt for example. To run the program, just type the below in the Command Prompt window:

stop stoplist50 docOrigAbassiM.txt

(note that the above includes the path name to tell stop where AbassiM.txt is – this is the docOrig folder).  This should cause a version of AbassiM.txt with stop words removed to be printed onto your screen.  You need to store this output in a text file AbassiM.stp.     To   keep   the   ‘stopped’   documents   separate   from   the   original documents, there is created folder in lab1-2024 called docStop.  All of the ‘stopped’ documents should go in this new folder.

You need to apply stop to all of the project description files.  To do this I have created a  batch  file  called  stopScript.bat,  which  you  should  have  in  your  lab1-2024 folder.  In the Command Prompt window just type stopScript followed by ‘return’ . You need to be in the lab1-2024 folder when you do this.

You should now have 112 files in the docStop folder, each with a name of the form filename.stp.

Question 1: What is the percentage reduction in the number of words in a document as a consequence of stop-word removal – specifically, what is the reduction in the case of the file AgricoleW.txt?

Task 2:    Stemming: The next task is to apply the porter stemmer to each ‘ .stp’ file.  There is created  another  folder  in  lab1-2024 called  docStem.    This  folder  will  contain  a stemmed version of each file from the docStop folder.

Basically, for each  .stp file you create a  .stm file by typing, for example, porter-stemmer docStopAbassiM.stp

This causes a ‘stemmed’ version of AbassiM.stp to be printed on screen.  You need this data to be stored in a file called docStem/AbassiM.stm.  You need to do this for every  .stp file.  To do this I have created another batch file called stemScript.bat, which you should have in your lab1-2024 folder.  In Command Prompt window just type stemScript followed by ‘return’ . You need to be in the lab1-2024folder when you do this.

Question 2: Find the file AgricoleW.stm. What are the results of applying the porter- stemmer to the words communications, sophisticated and transmissions?

You should now have:

–    112 original .txt documents in the folder docOrig

–    112 ‘stopped’ documents in the folder docStop

–    112 ‘stemmed’ documents in the folder docStem

Task 3:    Create the document index files: If you’ve forgotten what the document index is, or

what it is for, look again at the lecture slides.  The next task is to create 3 index files: one for the original  .txt documents, one for the  .stp documents, and one for the  .stm documents.

You should have the executable index.exe in your lab1-2024folder (or compile the program index.c if needed).

You should have a text file called textFileList in your lab1-2024 folder.  This is simply a list of all of the original .txt files – one file per line.  Type:

index textFileList

followed by ‘return’ .  After a short pause a text version of the index file will be printed on your screen.  You need to store this data in a file called textIndex.  Type:

index textFileList > textIndex

followed by ‘return’ .  Look at this index file (open it in a text editor such as Notepad) and try to understand the information it contains.  The lecture notes will help you.  The first part of the file gives the list of documents with their document length (this is not the length in bytes – see lecture notes if you are unclear).  The second part of the file gives the list of all words (ordered based on IDF) that occurred in the set of documents and information related to each word.  For each word (its position is indicated in front of the word name), there is the total number of times the word appeared (wordCount), number of documents it appeared in (docCount), and the IDF value of the word.  This is then followed with the list of documents the word appeared in, the count and calculated weight.

Now repeat this on the ‘stopped’ and ‘stemmed’ files:

index stopFileList > stopIndex

index stemFileList > stemIndex

Question 3:  What are the ‘document lengths’ of documents: docOrigDongP.txt, docStopDongP.stp and docStemDongP.stm?  Why are they different?  Why is the   difference    between   the   document    lengths   of    docStemDongP.stm and docOrigDongP.txt greater than the difference between the document lengths of docStopDongP.stp and docOrigDongP.txt?

Question 4: The IDF of the term design is approx. 0.009.  Why is it so close to zero?

Question 5 : Find the word algorithm in the three index files.  Explain why the entries for this word are different in the three files.

Task 4:    Retrieval:  The final task in this part of the lab is retrieval.  To do this you will need to create a query.  This is just a text file containing your query – you can create it using Notepad or Wordpad. An example query – in file query – is in your lab1-2024folder. This query just contains the text: circuits and devices

Next you need to apply stop word removal and stemming to the query:

stop stoplist50 query > query.stp

porter-stemmer query.stp > query.stm

You should have the executable retrieve.exe of the C program in your lab1-2024 folder (or compile the source C program if needed).  You can now do retrieval.

Start with the raw text files:

retrieve textIndex query

followed by ‘return’ .  This will return a list of all the documents for which the similarity with the  query  is  greater  than  0.    It  also  tells  you  the  identity  of  the  most  similar document.

Now  repeat  this  for  the  stopped  documents  and  stopped  query,  and  stemmed documents and stemmed query:

retrieve stopIndex query.stp

retrieve stemIndex query.stm

Question 6: Compare the results of the above two searches (using .stp and .stm) with the result for the original raw text files.  What do you conclude?

Question 7: Repeat Task 4 with one query of your own and report the results.

PART 2: LATENT SEMANTIC ANALYSIS

Objective

The objective of the second part of the lab is to apply Latent Semantic Analysis (LSA) to the set of BEng final year project specifications in the docOrig folder. Look at the notes on LSA to remind yourself about the technique, to put the following sequence of tasks into context.

Task 1:  Create the Word-Document matrix

Recall that the Word-Document matrix Wis an N x V matrix, where N is the number of documents and V is the vocabulary size (the number of different words in the corpus).  The nth  row of W is the document vector vec(dn) for the nth  document.

The executable doc2vec.exe of the C program will create the matrix W (or compile the source C program if needed).  We will apply this program to the stemmed documents.  The command is:

doc2vec stemFileList.txt > WDM

This creates a document vector for each document in the docStem folder and stacks them to create the matrix in the file  WDM.

Task 2:  Apply Singular Value Decomposition (SVD) to the Word-Document matrix

This is done in MATLAB.  You will need the following commands (the quote symbols used below should in Matlab be single quotes):

>>W=load(‘WDM’);

This reads the data in  WDM into the MATLAB matrix  W

>>[U,S,V]=svd(W);

This runs SVD on  W, decomposing it as  W = USVT.

Question 1: Are the matrices U and V as you would expect?  Explain.

Verify that the singular values, the diagonal elements of S, are ordered according to size.

Question 2: What are the values of the first 3 diagonal entries in S?

Now recall that the singular vectors, the ‘latent semantic classes’, correspond to the columns of V. You can access, for example, the first column of V and write it into the vector sv1 by using the MATLAB command:

>>sv1=V(:,1);

Do this for the first 3 columns of V, creating singular vectors sv1, sv2 and sv3.

Now you are going to try to interpret these vectors.  Intuitively, the most important words that determine the interpretation of the vector sv1 are those for which the corresponding coordinate of sv1 is biggest (positive or negative).

To find the biggest positive value in sv1 we can just use: >>m=max(sv1);

But we don’t just want to know the size of the biggest number, we also need to know its position in the vector so that we know which word it corresponds to.  So use:

>>[m,am]=max(sv1);

In this case m is the maximum value in sv1 and am is its index (argmax).   Find the words that correspond to the three biggest values in sv1.  To achieve this you need to know the order that the words occur in when the document vectors were constructed.  The program doc2vec.exe is based on index.exe, and the word order is the same in both programs.  So the nth  component of a document vector corresponds to the nth  word in the corresponding index file.  Hint, the most significant word for sv1 turns out to be ‘project’ .

Question 3:  Find the three most significant words for each of the singular vectors sv1, sv2 and sv3.  What is your interpretation of the corresponding semantic classes?