Information Retrieval Models

Ranks passages against queries using various models and techniques.

How to Run

The program can be initialised by running start.py, which accepts parameters in the format of: start.py <dataset> <model> [-s <smoothing>] [-p]

Parameters

Dataset

• The <dataset> parameter is required and is the path of the dataset to be parsed.
• Expects a TSV file in the format , where qid is the query ID, pid is the ID of the passage retrieved, query is the query text, and passage is the passage text,
• Each column must be tab separated.

Model

• The <model> parameter is required and is the name of the model to be used for ranking passages.
• Expects either 'bm25' for the BM25 model, 'vs' for the Vector Space model, or 'lm' for the query likelihood model.
• Any other input will be deemed invalid, and an exception will be raised.

Smoothing

• The -s <smoothing> parameter is required only when using the Query Likelihood model, and is the name of the smoothing technique which will be applied.
• Expects either 'laplace' for Laplace smoothing, 'lidstone' for Lidstone smoothing, or 'dirichlet' for Dirichlet smoothing.
• This parameter can only ever be used if the Query Likelihood model was selected for the <model> parameter, and an exception will be raised if any other model is used.

Frequency Plot

• The -p parameter is optional and generates a PNG file which displays term frequencies in graph format.
• By default, this file will be saved to the local directory as term-frequencies.png.

Examples

BM25 Model

start.py dataset/candidate_passages_top1000.tsv bm25

Vector Space Model

start.py dataset/candidate_passages_top1000.tsv vs

Query Likehood Model

start.py dataset/candidate_passages_top1000.tsv lm -s laplace
start.py dataset/candidate_passages_top1000.tsv lm -s lidstone
start.py dataset/candidate_passages_top1000.tsv lm -s dirichlet

Dependencies

• numpy
• matplotlib
• nltk
• num2words