GitHub - hammadkhann/Boolean-Retrieval-Model: The objective of this assignment is to make you understand how different indexes work in retrieving different query from a collection. You will create Inverted index and positional index for a set of collection to facilitate Boolean Model of IR. Inverted files and Positional files are the primary data structure to support the efficient determination of which documents contain specified terms and at which proximity. You also learn to process simple Boolean expression queries through this assignment. Datasets You are given two textual dataset for building inverted and positional index on it. Your dictionary must be written to disk, for each word in the lexicon you must store a file offset to the corresponding posting list, and finally, you should process the raw text collection only once (many real-word collections are so big that the cost of multiple scans is prohibitive). For the Quran dataset, each verse constitutes a document. For the Hadeeth dataset, each hadeeth constitutes a document. Query Processing In this assignment, all you need to implement an information retrieval model called Boolean Information Retrieval Model with some simplified assumptions. You will be provided with a bunch of overly simplified documents collection (corpus), you need to implement a simplified Boolean users queries that can only be formed by joining three terms (t1, t2 and t3) with ( AND, OR and NOT) Boolean operators. For example a user query may be of the form (t1 AND t2 AND t3). For positional queries, the query text contains “/” along with a k intended to return all documents that contains t1 and t2, k words apart. For /0 (zero) means phrase or adjacent terms. Basic Assumption for Boolean Retrieval Model 1. An index term (word) is either present (1) or absent (0) in the document. A dictionary contains all index terms. 2. All index terms provide equal evidence with respect to information needs. ( No frequency count necessary, but in next assignment it can be) 3. Queries are Boolean combinations of index terms at max 3. 4. Boolean Operators (AND, OR and NOT) are allowed. For examples: X AND Y: represents doc that contains both X and Y X OR Y: represents doc that contains either X or Y NOT X: represents the doc that do not contain X 5. Queries of the type X AND Y / 3 represents doc that contains both X and Y and 3 words apart. As we discussed during the lectures, we will implement a Boolean Model by creating a posting list of all the terms present in the documents. You are free to implement a posting list with your choice of data structures; you are only allowed to preprocess the text from the documents in term of tokenization in which you can do case folding and stop-words removal but no-stemming. The stop word list is also provided to you in assignments files. Your query processing routine must address a query parsing, evaluation of the cost, and through executing it to fetch the required list of documents. A command line interface is simply required to demonstrate the working model. You are also provided by a set of 10 queries, for evaluating your implementation. Coding can be done

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
.spyproject		.spyproject
Dataset		Dataset
__pycache__		__pycache__
BooleanRetrievalModel.py		BooleanRetrievalModel.py
InvertedIndex.json		InvertedIndex.json
PositionalIndex.json		PositionalIndex.json

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The objective of this assignment is to make you understand how different indexes work in retrieving different query from a collection. You will create Inverted index and positional index for a set of collection to facilitate Boolean Model of IR. Inverted files and Positional files are the primary data structure to support the efficient determinati…