Boolean Retrieval and Vector Space Model with Positional Indexing

wikipedia-reader.py

Takes some arguments as wikipedia article titles and save their contents into pages folder.
Takes RANDOM $number to save $number wikipedia article(s).

Examples:

python .\wikipedia-reader.py "Project I.G.I." "Dota 2"

python .\wikipedia-reader.py RANDOM 5

retrieval.py

Get files in pages directory and after cleaning and filtering, make dict(dict({float, float, list, int})) model which is word, documnet name and {TF_IDF, normalized TF-IDF, positional index, count}.

For changing to boolean retrival run file with arg boolean and vector for vector space model(default).

NOTE

stopwords and punkt from nltk is required.

import nltk
nltk.download(['punkt','stopwords'])

query.py

Takes query and search in models created in retrieval.py. (retrieval_inverted_index.dict and doc_names.list)
Also takes keywords (AND, OR, WITH, NEAR and NOT) for boolean retrieval.
For changing to boolean retrival run file with arg boolean and vector for vector space model(default).

Example:

Search: valve dota

Search: valve AND dota

Search: valve OR NOT dota

Search: valve NEAR5 dota

Search: valve WITH dota

Search: valve NOT WITH dota

Search: valve NOT NEAR10 dota

Search: \exit