• Tokenizing docs
• Deleting stop words
• Stemming

• A dictionary containing all words of our dataset
• Store frequency of each word in our dataset
• Sotre every document that a word showed up in it and its frequency and its position in this document

1. Select related document from inverted index
2. Apply query operations (Not and Phrase operations)
3. Score documents respected to:
   1. How many words in query exist in theis document
   2. How many times each word of query repeted in this document
4. Show top-5 results with related sentences of those documents

• Calculating tf.idf score and cosine similarity between a query and a document

• Index elimination using champions list to make faster our engine

Information Retrieval course Fall-1401 at Computer Engineering of Amirkabir University of Technology taught by Prof. Ahmad Nickabadi