Information Retrieval Models

Ranks passages against queries using various models and techniques.

Models

• BM25 - Probabilistic retrieval model for estimating the relevance of a passage.
• VectorSpace - Algebraic model for representing passages as vectors.
• QueryLikelihood - Language model for calculating the likelihood of a document being relevant to a given query.

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>]

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.

Examples

• start.py dataset.tsv bm25
• start.py dataset.tsv vs
• start.py dataset.tsv lm -s laplace

Dependencies

• numpy
• matplotlib
• nltk
• num2words
• tabulate
• punkt (nltk module)
• stopwords (nltk module)
  NLTK modules are downloaded automatically at runtime