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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