Incorporating Temporal Information in Entailment Graph Mining
Liane Guillou, Sander Bijl de Vroe, Mohammad Javad Hosseini, Mark Johnson, Mark Steedman
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Abstract
We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose play, while avoiding the pitfall of learning non-entailments such as win lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.