Description
This assignment has several parts: implementing a hash table similar to
those described in the text (with some additional functionality) and
writing an application that builds a concordance. A Webster’s
dictionary definition of concordance is: “an alphabetical list of the
main words in a work.” In addition to the main words, your program will
keep track of all the line numbers where these main words occur.
Word and Line Concordance Application
The goal of this assignment is to process a textual data file to
generate a word concordance with line numbers for each main word. A
Hash Table ADT is perfect to store the word concordance with the word
being the key and a list of its line numbers being the associated value
for the key. Since the concordance should only keep track of the “main”
words, there will be another file containing words to ignore, namely a
stop-words file (stop_words.txt). The stop-words file will contain a
list of stop words (e.g., “a”, “the”, etc.) — these words will not be
included in the concordance even if they do appear in the data file.
You should also not include strings that represent numbers. e.g. “24” or
“2.4” should not appear.
Sample Text File
“`
This is a sample data ((text)) file, to be processed by your word-concordance program!!!
A REAL data files is MUCH bigger. Gr8!
“`
Sample Stop-Words File
“`
a
about
be
by
can
do
i
in
is
it
of
on
the
this
to
was
“`
Sample Result File
“`
bigger: 4
concordance: 2
data: 1 4
file: 1 4
much: 4
processed: 2
program: 2
real: 4
sample: 1
text: 1
word: 2
your: 2
“`
Notes:
1. Words are defined as sequences of characters delimited by any non-letter (whitespace, punctuation).
1. There is no distinction made between upper and lower case letters (CaT is the same word as cat)
1. Blank lines are counted in line numbering.
The general algorithm for the word-concordance program is:
1. Read the stop-words file into your implementation of a hash table
containing the stop words. For the initial table size, start with
default of 191 and let the table grow as described below, if
necessary. Note: You should use the same hash table implementation
for the stop-words and the concordance. In the case of the
stop-words, you just won’t use the line number information (can
either store the actual line number from the file, or just use a
default value).
1. The word-concordance will be in a separate hash table from the stop
words hash table. Process the input file one line at a time to build
the word-concordance. This hash table should only contain the
non-stop words as the keys (use the stop words hash table to “filter
out” the stop words). Associated with each key is its value where
the value consists of a list containing the line numbers where the
key appears. DO NOT INCLUDE DUPLICATE LINE NUMBERS.
1. Generate a text file containing the concordance words printed out in
alphabetical order along with their line numbers. One word per line
(followed by a colon), and spaces separating items on each line:
data: 1 4
Note there is no space after the last line number – make sure to match the sample output files.
It is strongly suggested that the logic for reading words and assigning
line numbers to them be developed and tested separately from other
aspects of the program. This could be accomplished by reading a sample
file and printing out the words recognized with their corresponding line
numbers without any other word processing.
Collision resolution:
Your implementation should use Open Addressing using quadratic probing for collision resolution.
Note that you will not have to incorporate deleting items from your hash table
The hash function should take a string containing one or more characters
and return an integer. Use Horner’s rule to compute the hash
efficiently:
h(str) = ∑_(i=0)^(n-1)〖ord(str[i])* 〖31〗^(n-1-i) 〗 where n = the minimum of len(str) and 8
Also, your hash table size should have the capability to grow if the
input file is large. After insertion of an item, if the load factor
exceeds 0.5, you should grow the hash table size.
Start with a default hash table size of 191, then if increases are necessary, use:
“new table size” = 2 * “old table size” + 1 (use this “new table size” even if it is no longer a prime)
Removing Punctuation
It is recommended that you process the input file one line at a time.
For each line in the input file, do the following:
* Remove all occurrences of the apostrophe character (‘)
* Convert all characters in string.punctuation to spaces.
* Split the string into tokens using the .split() method.
* Each token that returns True when the is_alpha() method is called should be considered a “word”. All other tokens should be ignored.
Provided Data Files
* the stop words in the file stop_words.txt
* six sample data files that can be used for preliminary testing of your programs:
* file1.txt, file1_sol.txt – contains no punctuation to be removed
* file2.txt, file2_sol.txt – contains punctuation to be removed
* declaration.txt, declaration_sol.txt – larger file for test
