School of Computing

Information Retrieval

NUS SoC, 2012/2013, Semester II Video Conferencing Room (COM1 02 VCRm) / Mondays 10:00-12:00

Last updated: Wednesday, February 6, 2013 12:28:43 PM SGT - Added update about no skeleton code from email answer.

Homework #2 » Boolean Retrieval

In Homework 2, you will be implementing indexing and searching techniques for Boolean retrieval described in Lectures 2 and 3.


Your indexing script,, should be called in this format:

$ python -i directory-of-documents -d dictionary-file -p postings-file

Documents to be indexed are stored in directory-of-documents. In this homework, we are going to use the Reuters training data set provided by NLTK. Depending on where you specified the directory for NLTK data when you first installed the NLTK data (recall that the installation is triggered by, this data set is located under a path like:


On sunfire, recall that we installed the NLTK python module and its corpora under the class UNIX account. So the Reuters training data is here:


Recall that the dictionary is commonly kept in memory, with pointers to each postings list, which is stored on disk. This is because the size of dictionary is relatively small and consistent, while the postings can get very large when we index millions of documents. At the end of the indexing phase, you are to write the dictionary into dictionary-file and the postings into postings-file. For example, the following command writes the dictionary and postings into dictionary.txt and postings.txt.

$ python -i /home/course/cs3245/nltk_data/corpora/reuters/training/ -d dictionary.txt -p postings.txt

Although you can use any file names as you like, in this homework please follow the above command to use dictionary.txt and postings.txt, so that our marking script can easily locate the files.

In order to collect the vocabulary, you need to apply tokenization and stemming on the document text. You should use the NLTK tokenizers (nltk.sent_tokenize() and nltk.word_tokenize()) to tokenize sentences and words, and the NLTK Porter stemmer (class nlkt.stem.porter) to do stemming. You need to do case-folding to reduce all words to lower case.

Skip Pointers

You also need to implement skip pointers in the postings lists. Implement the method described in the lecture, where math.sqrt(len(posting)) skip pointers are evenly placed on the a postings list. Although implementing skip pointers takes up extra disk space, it provides a shortcut to efficiently merge two postings lists, thus boosting the searching speed.


Here is the command to run your searching script,

$ python -d dictionary-file -p postings-file -q file-of-queries -o output-file-of-results

dictionary-file and postings-file are the output files from the indexing phase. Queries to be tested are stored in file-of-queries, in which one query occupies one line. Your answer to a query should contain a list of document IDs that match the query in increasing order. In the Reuters corpus, the document IDs should follow the filenames (that is, your indexer should assign its document ID 1 to the filename named "1"; also note that while Reuters doc IDs are unique integers, they are not necessary sequential). For example, if three documents 12, 40 and 55 are found in the search, you should write "12 40 55" into output-file-of-results in one line. When no document is found, you should write an empty line. The results in output-file-of-results should correspond to the queries in file-of-queries.

Your program should not read the whole postings-file into memory, because in practice, this file may be too large to fit into memory when we index millions of documents. Instead, you should use the pointers in the dictionary to load the postings lists from the postings-file. Make sure you use the seek and read I/O functions that come from python's IO library for this.

The operators in the search queries include: AND, OR, NOT, (, and ). The operators will always be in UPPER CASE (lower case "and"s, "or"s and "not"s simply won't occur in your data (but you should probably bulletproof your code anyways). You can safely assume that there is no nested parentheses, for example, the query (a AND (b OR c)) will not occur. However, there only a light restriction on the length of the query (won't be over 1024 characters but can be long). Note that parentheses have higher precedence than NOT, which has a higher precedence than AND, which has a higher precedence than OR. AND and OR are binary operators, while NOT is a unary operator. Below is an illustration of a valid example query:

bill OR Gates AND (vista OR XP) AND NOT mac

While indexing is an off-line phase, searching is designed to be real-time (the extreme example is Google Instant), thus efficiency is very important in searching. In this homework, we won't be evaluating based on how fast your program can index a list of documents (the preprocessing), but we will test the efficiency of your searching program (runtime speed), as well as its accuracy.

What to turn in?

You are required to submit,, dictionary.txt, and postings.txt. Please do not include the Reuters data.

Essay questions

You are also asked to answer the following essay questions. These are to test your understanding of the lecture materials. Note that these are open-ended questions and do not have gold standard answers. A paragraph or two are usually sufficient for each question. You may receive a small amount of extra credit if you can support your answers with experimental results.

  1. You will observe that a large portion of the terms in the dictionary are numbers. However, we normally do not use numbers as query terms to search. Do you think it is a good idea to remove these number entries from the dictionary and the postings lists? Can you propose methods to normalize these numbers? How many percentage of reduction in disk storage do you observe after removing/normalizing these numbers?
  2. What do you think will happen if we remove stop words from the dictionary and postings file? How does it affect the searching phase?
  3. The NLTK tokenizer may not correctly tokenize all terms. What do you observe from the resulting terms produced by sent_tokenize() and word_tokenize()? Can you propose rules to further refine these results?

Submission Formatting

You are allowed to do this assignment individually or as a team of two. There will be no difference in grading criteria if you do the assignment as a team or individually.

For us to grade this assignment in a timely manner, we need you to adhere strictly to the following submission guidelines. They will help me grade the assignment in an appropriate manner. You will be penalized if you do not follow these instructions. Your matric number in all of the following statements should not have any spaces and any letters should be in CAPITALS. You are to turn in the following files:

These files will need to be suitably zipped in a single file called <matric number>.zip. Please use a zip archive and not tar.gz, bzip, rar or cab files. Make sure when the archive unzips that all of the necessary files are found in a directory called <matric number>. Upload the resulting zip file to the IVLE workbin by the due date: 4 March 2013, 11:59:59 pm SGT. There absolutely will be no extensions to the deadline of this assignment. Read the late policy if you're not sure about grade penalties for lateness.

Grading Guidelines

The grading criteria for the assignment is tentatively:

Disclaimer: percentage weights may vary without prior notice.