This repo consists of all the assignments, and, tasks of Information Retrieval course of FAST NUCES Spring 2023.

Link to Article: Boolean Retrieval Model

The Boolean retrieval model is a search model that retrieves documents that match a Boolean expression (a query) of terms, where the terms are connected by Boolean operators (AND, OR, NOT).

• install python 🐍 latest version and set it up on your computer.
• clone this repository in a specific folder.
• unzip the folder and open the assignment folder in a preferred code editor or IDE.

• Install Dependencies

  • pip install nltk
  • pip install unidecode
  • pip install pycontractions
  • pip install AST
  • pip install tk

• For Linux

  python3 main.py

• For Windows

  python main.py

• word

• not word

• word1 and word2
• word1 and word2 and word3
• not word1 and word2
• word1 and not word2
• not word1 and not word2
• not word1 and word2 and word3
• word1 and not word2 and word3
• word1 and word2 and not word3
• not word1 and not word2 and not word3

• word1 or word2
• word1 or word2 or word3
• not word1 or word2
• word1 or not word2
• not word1 or not word2
• not word1 or word2 or word3
• word1 or not word2 or word3
• word1 or word2 or not word3
• not word1 or not word2 or not word3

• It includes query with mixed boolean opearators (AND, OR, NOT) and word limit upto 3 words max.

• word1 word2 \k, here 'k' represents the no. of words word2 is distant from word1.

The Vector Space Model is a commonly used information retrieval technique where documents are represented as vectors in a high-dimensional space and ranked based on their similarity to a user's query.

• install python 🐍 latest version and set it up on your computer.
• clone this repository in a specific folder.
• unzip the folder and open the assignment folder in a preferred code editor or IDE.

• Install Dependencies

  • pip install nltk
  • pip install unidecode
  • pip install pycontractions
  • pip install AST
  • pip install tk

• For Linux

  python3 main.py

• For Windows

  python main.py

The results are written after applying threshold using alpha value= 0.35

Results Format: Query
Ranked Documents
Respective Cosine Similarities

alpha value=0.35
threshold formula= max(cosine sim)* alpha value

• cricket politics
  [5, 26, 29, 14, 25, 3, 24]
  [0.10483768357856904, 0.0733026052948556, 0.05747687349155568, 0.051814006141766254, 0.04669640019650995, 0.044924892358261706, 0.04204193256553604]
  
• dharamsala to indore
  [17]
  [0.5669900905084869]
  
• retirement
  [14]
  [0.05884903818579923]
  
• test captain
  [3, 6, 21, 17]
  [0.2169788724831718, 0.16412707621815958, 0.12857575540015573, 0.109537249881213]
  
• pcb psl
  [11, 29]
  [0.3987613437849497, 0.23786386254409053]
  
• hate
  []
  []
  
• bowling coach
  [29, 6, 24]
  [0.15946207811500823, 0.10025156193891573, 0.06560991594156915]
  
• relative comfort
  [19, 11, 2, 7]
  [0.034858940263823475, 0.03152703135309784, 0.013545725571459473, 0.0126389307857281]
  
• possible
  [23, 15]
  [0.05534812091736982, 0.051743525151666955]
  
• batter bowler
  [2, 3, 16, 15, 30, 9]
  [0.09290690948714346, 0.08726633903001002, 0.05206958459212497, 0.03995088457437994, 0.03776960712447397, 0.032756883132567645]