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LM Data Mining and Machine Learning
创建日期：2024-05-16 14:37:08
标签： Machine
LM Data Mining and Machine Learning

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LM Data Mining and Machine Learning 


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 lab1-2022 from Canvas and ‘unzip’ it. You should end up with a new
folder called lab1-2022 containing all of the files that you need to complete the lab, including
a folder called docOrig which contains 112 text files.

The folder lab1-2022 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 lab1-2022 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 and porter-stemmer.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
lab1-2022 folder.
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 docOrig\AbassiM.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 lab1-2022 called docStop. All of the
‘stopped’ documents should go in this new folder.
2


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 lab1-2022
folder. In the Command Prompt window just type stopScript followed by ‘return’.
You need to be in the lab1-2022 folder when you do this.

You should now have 112 files in the docStop sub-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?
Answer:



Task 2: Stemming: The next task is to apply the porter stemmer to each ‘.stp’ file. There is
created another sub-folder of lab1-2022 called docStem. This folder will contain a
stemmed version of each file in the docStop folder.

Basically, for each .stp file you create a .stm file by typing, for example,

porter-stemmer docStop\AbassiM.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 lab1-2022 folder. In Command
Prompt window just type stemScript followed by ‘return’. You need to be in the
lab1-2022 folder 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?
Answer:



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.

3

You should have the executable index.exe in your lab1-2022 folder (or compile
the program index.c if needed).

You should have a text file called textFileList in your lab1-2022 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 the documents:
docOrig\DongP.txt, docStop\DongP.stp and docStem\DongP.stm? Why
are they different? Why is the difference between the document lengths of
docStem\DongP.stm and docOrig\DongP.txt greater than the difference
between the document lengths of docStop\DongP.stp and
docOrig\DongP.txt?
Answer:



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



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



4

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 – query – should be in your lab1-2022
folder. This query just contains the text: communication and networks

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-
2022 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. Does the result make sense?

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 these two searches with the result for the original
raw text files. What do you conclude?
Answer:



Task 5: Question 7: Repeat Task 4 with 2 queries of your own and report the results.
Answer:

5

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. This
part is quite challenging, so you may have problems!

Task 1: Create the Word-Document matrix
Recall that the Word-Document matrix W is 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:

>>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.
Answer:



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?
Answer:



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.

6

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?
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