Java代写 | CMPT 456 Course Project 1  

CMPT 456 Course Project 1  
The purpose of having Lucene/Solr running inside a Docker container is to help you work  
on this assignment using mostly any OS you prefer, Linux, Mac or Windows. If you are  
curious about how the Docker container is built, look at the Dockerfile in the source code.  
Project Data  
We have included the data for you, within the codebase at location lucene/demo/data.  
In the subsequent sections, you will use it in to demonstrate indexing and querying  
Build Docker image from the source code (make sure that we have. (i.e. current location)  
at the end of the command):  
docker build -t cmpt456-lucene-solr:6.6.7.  
NOTE: Since Docker is not available free for Windows OS, we recommend you use VirtualBox with  
Ubuntu OS or Windows Subsystem for Linux (WSL)  
Run the Docker image we just built in order to activate the Docker container:  
docker run -it cmpt456-lucene-solr:6.6.7  
In this section, we help you to get familiar with Lucene basic components by running 2 simple  
Index Files: this program uses standard analyzers to create tokens from input text files,  
convert them to lowercase then filer out predefined list of stop-words.  
is stored  
in this file  
within the  
Search Files: this program uses a query parser to parse the input query text, then pass to  
the index searcher to look for matching results.  
is stored  
in this file  
within the  
You are expected to run these examples, understand Lucene components used in the indexing  
and querying process in order to make further extensions in the below programming tasks.  
Text Parsing (30 pts)  
In the first part of the assignment, you will learn how to use Lucene to build search capabilities  
for documents in various formats, such as HTML, XML, PDF, Word. In fact, Lucene does not care  
about the parsing of these and other document formats, and it is the responsibility of the  
application using Lucene to use an appropriate parser to convert the original format into plain  
text before passing that plain text to Lucene.  
In the class within the Demo section, you can see that it indexes the content of  
html files, including all html tags (e.g., <body>, <head>, <table>). In this section, we want you to  
create a new class called to:
Use a HTML parser to parse input files to extract the title and text content only of the  
HTML files. Text content should not contain any HTML tags.  
Use standard analyzers to create tokens from the result of parser, convert them to  
lowercase then filter out based on a predefined list of stop-words (similar to the way works)  
Tokenization (30 pts)  
In the second part of the assignment, you will experience how plain text passed to Lucene for  
indexing goes through a process generally called tokenization. Tokenization is the process of  
breaking input text into small indexing elements – tokens. The way input text is broken into  
tokens heavily influences how people will then be able to search for that text.  
As you have seen in the, we have used class StandardAnalyzer in order to control  
the tokenization process. Look at its source code, you can see this class extends the  
createComponents method to build a standard tokenization process to convert tokens to  
lowercase then filer out based on a predefined list of stop-words.  
In this section, we want you to create a class called to control the  
tokenization process as follows:  
Hint: Porter stemmer is already implemented in Lucene. Make use of it.  
Similarity Metrics (40 pts)  
In the last part of the assignment, you will have chance to touch one of the core modules of  
querying process which is the ranking module. When user issues a query, Lucene will use index  
created during the indexing process to look for matching documents. More importantly, these  
matching documents will be sorted by a customizable ranking function before returning the final  
results to the user.  
Before asking you to implement a ranking function, we want you to make use of Lucene to  
compute some basic metrics:  
Next, we want you to implement a custom ranking/similarity function base on TFIDFSimilarity  
arity.html) provided by Lucene. In particular, you need to create a class called to support custom tf() and idf() as follows:  
ꢁ(ꢀ ∈ ꢂ) = (1 + ꢁꢃꢄꢅꢆꢄꢇꢈꢉ)  
ꢂꢌꢈꢎꢌꢆꢇꢀ + 2  
ꢊꢂꢁ(ꢀ) = 1 + ꢋꢌꢍ 5 + 2  


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