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This is a Python project that performs tokenization, stop word removal, positional indexing, phrase query searching, term frequency-inverse document frequency (TF-IDF) calculation, cosine similarity computation, and document ranking.

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abdulrahmanaymann/Data-Storage-and-Information-Retrieval

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Data Storage and Information Retrieval

This is a Python project that performs tokenization, stop word removal, positional indexing, phrase query searching, term frequency-inverse document frequency (TF-IDF) calculation, cosine similarity computation, and document ranking. The project consists of three parts, each of which is described in detail below.

Part 1: Tokenization and Stop Word Removal

This part reads 10 text files and applies tokenization to each file to split it into individual words. Stop words are also removed from the text, except for the words "in" and "to".

Part 2: Positional Indexing and Phrase Query Searching

The second part of the project builds a positional index for the text files and displays each term with the number of documents containing the term, as well as the positions of the term in each document. The system also allows users to search for a phrase in the text using the positional index, and returns the documents that match the query.

Part 3: TF-IDF Calculation, Cosine Similarity Computation, and Document Ranking

The final part of the project computes the term frequency and inverse document frequency for each term in each document, and displays the resulting TF-IDF matrix. The system then computes the cosine similarity between the query and each matched document, and ranks the documents based on their similarity to the query.

Usage

To use this project, follow these steps:

  1. Clone the repository to your local machine.
  2. Install any required dependencies.
  3. Run the main.py script to execute the project.

Technologies

  • Python
  • numpy
  • pandas
  • nltk

Output

Term Frequency (TF)

Term d1 d2 d3 d4 d5 d6 d7 d8 d9 d10
antony 1 1 0 0 0 1 0 0 0 0
brutus 1 1 0 1 0 0 0 0 0 0
caeser 1 1 0 1 1 1 0 0 0 0
calpurnia 0 1 0 0 0 0 0 0 0 0
cleopatra 1 0 0 0 0 0 0 0 0 0
mercy 1 0 1 1 1 1 0 0 0 0
worser 1 0 1 1 1 0 0 0 0 0
angels 0 0 0 0 0 0 1 1 1 0
fools 0 0 0 0 0 0 1 1 1 1
fear 0 0 0 0 0 0 1 1 0 1
in 0 0 0 0 0 0 1 1 1 1
rush 0 0 0 0 0 0 1 1 1 1
to 0 0 0 0 0 0 1 1 1 1
tread 0 0 0 0 0 0 1 1 1 1
where 0 0 0 0 0 0 1 1 1 1

w-tf(1+ log tf) Results

Term d1 d2 d3 d4 d5 d6 d7 d8 d9 d10
antony 1 1 0 0 0 1 0 0 0 0
brutus 1 1 0 1 0 0 0 0 0 0
caeser 1 1 0 1 1 1 0 0 0 0
calpurnia 0 1 0 0 0 0 0 0 0 0
cleopatra 1 0 0 0 0 0 0 0 0 0
mercy 1 0 1 1 1 1 0 0 0 0
worser 1 0 1 1 1 0 0 0 0 0
angels 0 0 0 0 0 0 1 1 1 0
fools 0 0 0 0 0 0 1 1 1 1
fear 0 0 0 0 0 0 1 1 0 1
in 0 0 0 0 0 0 1 1 1 1
rush 0 0 0 0 0 0 1 1 1 1
to 0 0 0 0 0 0 1 1 1 1
tread 0 0 0 0 0 0 1 1 1 1
where 0 0 0 0 0 0 1 1 1 1

DF-IDF Results

Term df idf
antony 3 0.5228787
brutus 3 0.5228787
caeser 5 0.30103
calpurnia 1 1
cleopatra 1 1
mercy 5 0.30103
worser 4 0.39794
angels 3 0.5228787
fools 4 0.39794
fear 3 0.5228787
in 4 0.39794
rush 4 0.39794
to 4 0.39794
tread 4 0.39794
where 4 0.39794

TF.IDF Results

Term d1 d2 d3 d4 d5 d6 d7 d8 d9 d10
antony 0.5228787 0.5228787 0 0 0 0.5228787 0 0 0 0
brutus 0.5228787 0.5228787 0 0.5228787 0 0 0 0 0 0
caeser 0.30103 0.30103 0 0.30103 0.30103 0.30103 0 0 0 0
calpurnia 0 1 0 0 0 0 0 0 0 0
cleopatra 1 0 0 0 0 0 0 0 0 0
mercy 0.30103 0 0.30103 0.30103 0.30103 0.30103 0 0 0 0
worser 0.39794 0 0.39794 0.39794 0.39794 0 0 0 0 0
angels 0 0 0 0 0 0 0.5228787 0.5228787 0.5228787 0
fools 0 0 0 0 0 0 0.39794 0.39794 0.39794 0.39794
fear 0 0 0 0 0 0 0.5228787 0.5228787 0 0.5228787
in 0 0 0 0 0 0 0.39794 0.39794 0.39794 0.39794
rush 0 0 0 0 0 0 0.39794 0.39794 0.39794 0.39794
to 0 0 0 0 0 0 0.39794 0.39794 0.39794 0.39794
tread 0 0 0 0 0 0 0.39794 0.39794 0.39794 0.39794
where 0 0 0 0 0 0 0.39794 0.39794 0.39794 0.39794

Document Length

Document Length
d1 1.3734623
d2 1.2796185
d3 0.4989743
d4 0.782941
d5 0.5827473
d6 0.6742702
d7 1.2234958
d8 1.2234958
d9 1.1061373
d10 1.1061373

Normalized TF.IDF

Term d1 d2 d3 d4 d5 d6 d7 d8 d9 d10
antony 0.3807012 0.4086208 0 0 0 0.7754736 0 0 0 0
brutus 0.3807012 0.4086208 0 0.6678393 0 0 0 0 0 0
caeser 0.219176 0.2352498 0 0.3844862 0.5165704 0.4464531 0 0 0 0
calpurnia 0 0.7814829 0 0 0 0 0 0 0 0
cleopatra 0.728087 0 0 0 0 0 0 0 0 0
mercy 0.219176 0 0.6032976 0.3844862 0.5165704 0.4464531 0 0 0 0
worser 0.2897349 0 0.7975161 0.5082631 0.682869 0 0 0 0 0
angels 0 0 0 0 0 0 0.4273646 0.4273646 0.4727069 0
fools 0 0 0 0 0 0 0.3252484 0.3252484 0.3597564 0.3597564
fear 0 0 0 0 0 0 0.4273646 0.4273646 0 0.4727069
in 0 0 0 0 0 0 0.3252484 0.3252484 0.3597564 0.3597564
rush 0 0 0 0 0 0 0.3252484 0.3252484 0.3597564 0.3597564
to 0 0 0 0 0 0 0.3252484 0.3252484 0.3597564 0.3597564
tread 0 0 0 0 0 0 0.3252484 0.3252484 0.3597564 0.3597564
where 0 0 0 0 0 0 0.3252484 0.3252484 0.3597564 0.3597564

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This is a Python project that performs tokenization, stop word removal, positional indexing, phrase query searching, term frequency-inverse document frequency (TF-IDF) calculation, cosine similarity computation, and document ranking.

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