You will implement a little search engine to do two things: (a) gather and index keywords that appear in a
set of plain text documents, and (b) search for user-input keywords against the index and return a list of
matching documents in which these keywords occur.
Download the attached lse_project.zip file to your computer. DO NOT unzip it. Instead, follow the
instructions on the Eclipse page under the section “Importing a Zipped Project into Eclipse” to get the
entire project, called Little Search Engine, into your Eclipse workspace.
Here are the contents of the project:
A class, lse.LittleSearchEngine. This is where you will fill in your code, details follow.
A supporting class, lse.Occurrence, which you will NOT change.
Two sample text documents, AliceCh1.txt, and WowCh1.txt, directly under the project folder,
for preliminary testing. Be sure to get other online text documents–or make your own–for more
A noisewords.txt file that contains a list of “noise” words, one per line. Noise words are
commonplace words (such as “the”) that must be ignored by the search engine. You will use this file
(and this file ONLY) to filter out noise words from the documents you read, when gathering
A docs.txt file that has a list of all documents (in this case AliceCh1.txt and WowCh1.txt)
from which the search engine should extract keywords.
NOTE: You will need to write your own driver to test your implementation. This driver can take as inputs a
file that contains the names of all the documents (such as docs.txt), as well as the noisewords.txt
file. It can then set up a LittleSearchEngine object and call its methods as needed to test the
implementation. The docs.txt and noisewords.txt filenames will be sent in as the arguments to the
makeIndex method in LittleSearchEngine.
Following is the sequence of method calls that will be performed on a LittleSearchEngine object, to
index and search keywords.
LittleSearchEngine() – Already implemented.
The constructor creates new (empty) keywordsIndex and noiseWords hash tables. The
keywordsIndex hash table is the MASTER hash table, which indexes all keywords from all input
documents. The noiseWords hash table stores all the noise words. Both of these are fields in the
Every key in the keywordsIndex hash table is a keyword. The associated value for a keyword is
an array list of (document,frequency) pairs for the documents in which the keyword occurs,
arranged in descending order of frequencies. A (document,frequency) pair is held in an
Occurrence object. The Occurrence class is defined in the LittleSearchEngine.java file,
at the top. In an Occurrence object, the document field is the name of the document, which is
basically the file name, e.g. AliceCh1.txt.
void makeIndex(String docsFile, String noiseWordsFile) – Already implemented.
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Indexes all the keywords in all the input documents. See the method documentation and body in the
LittleSearchEngine.java file for details.
If you want to index the given sample documents, the first parameter would be the file docs.txt
and the second parameter would be the noise words file, noisewords.txt
After this method finishes executing, the full index of all keywords found in all input documents will
be in the keywordsIndex hash table.
The makeIndex methods calls methods loadKeywordsFromDocument and mergeKeywords,
both of which you need to implement.
HashMap<String,Occurrence> loadKeywordsFromDocument(String docFile) –
This method creates a hash table for all keywords in a single given document. See the
method documentation for details.
This method MUST call the getKeyword method, which you need to implement.
String getKeyword(String word) – You implement.
Given an input word read from a document, it checks if the word is a keyword, and
returns the keyword equivalent if it is.
FIRST, see the method documentation in the code for details, including a specific short
list of punctuations to consider for filtering out. THEN, look at the following illustrative
examples of input word, and returned value.
Input Parameter Returned value
distance. distance (strip off period)
equi-distant null (not all alphabetic characters)
Rabbit rabbit (convert to lowercase)
Through null (noise word)
we’re null (not all alphabetic characters)
World… world (strip trailing periods)
World?! world (strip trailing ? and !)
What,ever null (not all alphabetic characters)
Observe that (as per the rules described in the method documentation), if there is more than
one trailing punctuation (as in the “World…” and “World?!” examples above), the method
strips all of them. Also, the last example makes it clear that punctuation appearing anywhere
but at the end is not stripped, and the word is rejected.
Note that this is a much simplified filtering mechanism, and will reject certain words that might
be accepted by a real-world engine. But the idea is to not unduly complicate this process,
focusing instead on hash tables, which is the point of this assignment. So, just stick to the
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rules described here.
void mergeKeywords – You implement.
Merges the keywords loaded from a single document (in method
loadKeywordsFromDocument) into the global keywordsIndex hash table.
See the method documentation for details. This method MUST call the
insertLastOccurence method, which you need to implement.
occs) – You implement.
See the method documentation for details. Note that this method uses binary search
on frequency values to do the insertion. The return value is the sequence of mid points
encountered during the search, using the regular (not lazy) binary search we covered
in class. This return value is not used by the calling method-it is only going to be used
for grading this method.
For example, suppose the list had the following frequency values (including the last
one, which is to be inserted):
12 8 7 5 3 2 6
0 1 2 3 4 5 6
Then, the binary search (on the list excluding the last item) would encounter the
following sequence of midpoint indexes:
2 4 3
Note that if a subarray has an even number of items, then the midpoint is the last
item in the first half.
After inserting 6, the input list would be updated to this:
12 8 7 6 5 3 2
0 1 2 3 4 5 6
and the sequence 2 4 3 would be returned.
If the new item is a duplicate of something that already exists, it doesn’t matter if
the new item is placed before or after the existing item.
Note that the items are in DESCENDING order, so the binary search would have to be
ArrayList top5search(String kw1, String kw2) – You implement.
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This method computes the search result for the input “kw1 OR kw2”, using the keywordsIndex
hash table. The result is a list of names of documents (same as name of the text file for that
document), limited to the top 5 in which either of the words “kw1” or “kw2” occurs, arranged in
descending order of frequencies. See the method documentation in the code for additional
As an example, suppose the search is for “deep or world”, in the given test documents,
AliceCh1.txt (call it A) and WowCh1.txt (call it W). The word “deep” occurs twice in A and once
in W, and the word “world” occurs once in A and 7 times in W:
deep: (A,2), (W,1)
world: (W,7), (A,1)
The result of the search is:
in that order.
If there are no matches for either keyword, return null or empty list, either is fine.
If a document occurs in both keywords’ match list, consider the one with the higher frequency
– do NOT add frequencies.
Return AT MOST 5 non-duplicate entries. This means if there are more than 5 non-duplicate
entries, then return the five top frequency entries, but if there are fewer than 5 non-duplicate
entries, then return all of them.
If a document in the first match list (for the first keyword) has the same frequency as a
document in the second match list (for the second keyword), and both are candidates for
inclusion in the output (they are not the same document), then pick the document in the first
list before the document in the second list.
You may NOT MAKE ANY CHANGES to the LittleSearchEngine.java file EXCEPT to (a) fill in the
body of the required methods, or (b) add private helper methods.
You may NOT MAKE ANY CHANGES to the Occurrence class (you will only be submitting
LittleSearchEngine.java). When we test your submission, we will use the exact same version of
Occurrence that we shipped to you.
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When a method is graded, the correct versions of other methods will be used. Also, all data structures will
be set to their correct (expected) states before a method is called.
You need not do any error checking in your program for bad inputs.
Submit your LittleSearchEngine.java file.