A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like "after", which overtly signal a temporal relation. We first show that models trained on main and subordinate clauses connected with a temporaldoi:10.1613/jair.2015 fatcat:qan3gyxqxvbwzajzz5qh7r4uda