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COMP6714 Ranked Retrieval
创建日期：2024-06-06 18:05:40
标签： COMP6714
Ranked Retrieval

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COMP6714  Ranked Retrieval

In this project, you are going to implement (using Python3 in CSE linux machines) a simple search engine that ranks the output documents based on the promixity of the matching terms. A search query in this
project is a list of space-separated search terms and each search term may contain any numeric digits or uppercase/lowercase letters, and will not contain any punctuations. You will need to implement an indexer
(to index the files) and a search program (to search the files based on the index generated by your indexer). As a core requirement for this project, you must implement your solution using an inverted index with
positional information (for example, the positional index described in Lecture Week 1). You may also implement any additional indexes as appropriate, if you wish.
Given a search query, each matching document from the search result must contains terms that match all the search terms following the below term matching rules:
Search is case insensitive.
Full stops for abbreviations are ignored. e.g., U.S., US are the same.
Singular/Plural is ignored. e.g., cat, cats, cat's, cats' are all the same.
Tense is ignored. e.g., breaches, breach, breached, breaching are all the same.
A sentence can only end with a full stop, a question mark, or an exclamation mark.
Numeric tokens such as years, integers should be indexed accordingly and searchable.
Commas in numeric tokens are ignored, e.g., 1,000,000 and 1000000 are the same.
Except the above, all other punctuation should be treated as token dividers.
Numbers with decimal places can be ignored from the index, if you wish, as a decimal number is not a valid search term (since '.' is not allowed).
As a requirement of this project, the matching documents in a search result are ranked according to the distances between the matching terms in these documents, such that matching terms closer to each other
will be ranked higher than those further apart. Further details are described below in the Ranking section.
You are provided with approximately 1000 small documents (named with their document IDs) available in ~cs6714/reuters/data. You can find these files by logging into CSE machines and going to folder
~cs6714/reuters/data. Your submitted project will be tested against a similar collection of up to 1000 documents (i.e., we may replace some of these documents to avoid any hard-coded solutions).
Your submission must include 2 main programs: index.py and search.py as described below. It is your responsibility to submit any other auxiliary Python files if they are needed for the 2 main programs to work
properly.
This project will be marked based on auto marking, and will then be checked manually for other requirements described in this specification (for example, if a positional index is implemented). To ensure your
project satisfies the input and output formatting requirements, a simple sanity test script is available in ~cs6714/reuters/sanity. You should run the sanity test script before you submit your solution. To run the
sanity test script on a CSE linux machine, simply go inside the folder that contains your index.py and search.py and type: ~cs6714/reuters/sanity that will run tests based on examples presented below. Note that it
is only a sanity test primarily for formatting, and you are expected to test your project more thoroughly.
The Indexer
Your indexer is run by
python3 index.py [folder-of-documents] [folder-of-indexes]
where [folder-of-documents] is the path to the directory for the collection of documents to be indexed and [folder-of-indexes] is the path to the directory where the index file(s) should be created. All the files in
[folder-of-documents] should be opened as read-only, as you may not have the write permission for these files. If [folder-of-indexes] does not exist, create a new directory as specified. You may create multiple
index files although too many index files may slow down your performance. The total size of all your generated index files shall not exceed 20MB (which should be plenty for this project).
After the indexing is completed, it will output the total number of documents, the total number of tokens (after any preprocessing and filtering) to be indexed, and the total number of terms to be indexed. The
following example illustrates the required input and output formats:
$ python3 index.py ~cs6714/reuters/data ./MyTestIndex
Total number of documents: 1000
Total number of tokens: 260759
Total number of terms: 9509
$
Note: Each line of the output of index.py ends with one newline ('\n') character; and except for the total number of documents, your indexer may have different numbers of tokens and terms depending on your
preprocessing choices.
The Search
Your search program is run by
python3 search.py [folder-of-indexes]
where [folder-of-indexes] is the path to the directory containing the index file(s) that are generated by the indexer. After the above command is executed, it will accept a search query from the standard input and
output the result to the standard output as a sequence of document names (the same as their document IDs), one per line and sorted according to their ranking as decribed in the Ranking section below. It will then
continue to accept the search queries from the standard input and output the results to the standard output until the end (i.e., a Ctrl-D). The following example illustrates the required input and output formats:
$ python3 search.py ~/Proj/MyTestIndex
Apple
1361
Malaysia
311
908
1356
1675
2956
3051
3438
5169
5195
5216
5258
5285
5382
Australia Technology
3454
271 billions
880
$
Ranking
A proximity distance is defined as the number of terms (ignoring decimal numbers when counting) between each pair of terms that match two search terms. The search result is sorted by its minimum sum of the
proximity distances between the matching terms (left-to-right by query terms) in an ascending order, then by the number of matching terms occurring in the same order as the search terms, then by the numeric
values of the documentIDs (e.g., 72 will be output before 125). To elaborate this further, consider an example of 4 documents with document IDs 1-4:
DocID Content
1 apple durian cherry bread egg fennel garlic ham
2 bread garlic ham
3 egg bread cherry apple egg fennel ham garlic bread
4 ham garlic bread
Given a search query of "garlic bread", all documents contain these two search terms. The minimum proximity distance between the matching terms for these two terms are calculated as below:
Query: garlic bread
Expected output:
DocID Distance
3 0
4 0
2 0
1 2
For example, in Document 3, bread appears twice. We pick the second bread to calculate the proximity distance as it will result in a minimum value since it is next to garlic, i.e., with a distance 0. We define the
matching term that is used to calculate the minimum proximity distance as the closest matching term. Although Documents 2, 3, 4 are all having the minimum distance of 0, Documents 3 and 4 rank higher than
Document 2 since the two closest matching terms "garlic bread" appear in the same order as the search query, while Document 2 has a different order (i.e., "bread garlic"). Furthermore, since Documents 3 and 4
have the same minimum distance and the closest matching terms are in the same order, they are then sorted by their documentIDs. Finally, Document 1 has the largest minimum distance (2) and hence has the
lowest ranking.
Consider another search query "egg ham bread" for the example documents above. Only two documents contain terms matching all three search terms.
Query: egg ham bread
Expected output:
DocID Distance
3 1+1=2
1 2+3=5
For Document 3, there is more than one term matching egg and bread respectively. The second egg and the second bread are chosen as the closest matching terms to achieve the minimum sum of the proximity
distances between the 3 matching terms for the 3 search terms. As a result, Document 3 has a smaller minimum distance than Document 1 and hence it ranks higher.
Using the real documents available in ~cs6714/reuters/data, the following examples present their expected ranked output from your search.py and you can study their ranking further by inspecting the content of
their corresponding documents.
$ python3 search.py ~/Proj/MyTestIndex
australia technology
3454
bank expect distribution
3077
4367
4019
875
$
Since the input is from the standard input, your search engine should be able to accept input redirected from a file as shown below:
$ cat test1.txt
US finance COMPANY investor
$ python3 search.py ~/Proj/MyTestIndex < test1.txt
5171
3396
3023
5778
1682
$
Regarding the number of matching terms occurring in the same order as the search terms, the following post extracted from the Ed forum provides a good example. Consider two documents:
1. butter apple duck chicken
2. chicken butter apple duck
and a search query "apple butter chicken duck".
Both documents have 1 pair in matching order. You may consider it in a similar way to the proximity calculation, i.e. pair-wise, so if apple-butter is in correct order, then +1, etc. For this example:
butter apple duck chicken: +1 as butter-chicken is correct order
chicken butter apple duck: +1 as chicken-duck is correct order
Displaying Lines Containing Matching Terms
Given a search query starting with '>' and a space, in addition to the displaying the matching document IDs, the lines of text that contain the closest matching terms are also displayed according to the following
rules:
Each matching document ID is displayed with '>' and a space in front, followed by lines of text that containing the closest matching terms.
For each matching document, only one line of text that contains its corresponding closest matching term is displayed for each search term.
Lines of text are displayed according to the order of lines in a document, i.e., output line 1 before line 2.
In case more than one closest matching term is determined for a search term and they are at different lines, only the first line of these lines is displayed. This includes the case when a search query only
consists of one search term such that matching terms can be found in multiple lines (refer to the apple example below).
The examples below illustrate some of these rules and the display format.
$ python3 search.py ~/Proj/MyTestIndex
> bank expect distribution
> 3077
The bank said it expects the distribution will be made in
> 4367
Closing is expected to take place in early April and the
The partnership will acquire the refining and distribution
facility with U.S. and foreign banks to finance inventories and
> 4019
It said it did not know when distributions would be made.
The bank said it expected to report positive earnings in
> 875
prepared to negotiate a new distribution based on objective
bank debt, have increased political pressure on the country to
The expected drop in prices could result in losses of as
> AUStralia Technology
> 3454
marketing of high-technology smelting processes invented in
Australia, notably the Siromelt Zinc Fuming Process.
> apple
> 1361
The department said stocks of fresh apples in cold storage
$
Marking
This project is worth 40 points. Your submission will be tested and marked on CSE linux machines using Python3. Therefore, please make sure you have tested your solution on these machines using Python3
before you submit. You will not receive any marks if your program does not work on CSE linux machines and only works in other environment such as your own laptop. Full marks will be awarded to
submissions that follow this specification and pass all the test cases.
Although we do not measure the runtime speed, your indexing program will be terminated if it does not end after one minute, and you will receive zero marks for the project (since we cannot get the index
generated successfully for further testing); and your search program will be terminated if it does not end after 10 seconds per search query, and you will receive zero marks for that search query.
Partial Marks
For this project, a search result from your search engine is only considered correct if it contains exactly the same set of document names as the expected answer and their order must be exactly the same as well.
We will grant you some partial marks based on F-measure to evaluate how close your result is to the expected answer (in which any correct document names that are in wrong order will be treated as wrong
documents). F-measure = 2 * (precision * recall) / (precision + recall) will be used to calculate the final score for each test when your search results differ from the expected output. The final score for each test
will be calculated by: [F-measure round to 2 decimal places] * [fullMarks of that test]. The following examples further illustrate how this scheme works. Given the expected answer Ans, suppose that there are 6
results returned from 6 different search programs.
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