Here, LaSER takes a query entity and a language as input and recommends a language-specific ranking of events as output using:
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Given a query entity and using an embedding model, candidate_generation.py creates a set of candidate events.
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feature_extraction.py returns individual and pair features for candidate events.
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Using training dataset, LTR_training.py trains a ranker which would finally be used on extracted features of candidate events to rank candidates.
Distributed under the MIT License. See LICENSE for more information.