Language-Independent Discriminative Parsing of Temporal Expressions

Temporal resolution systems are tradition-ally tuned to a particular language, re-quiring significant human effort to trans-late them to new languages. We present a language independent semantic parser for learning the interpretation of tempo-ral phrases given only a corpus of utter-ances and the times they reference. We make use of a latent parse that encodes a language-flexible representation of time, and extract rich features over both the parse and associated temporal semantics. The parameters of the model are learned using a weakly supervised bootstrapping approach, without the need for manually tuned parameters or any other language expertise. We achieve state-of-the-art ac-curacy on all languages in the TempEval-2 temporal normalization task, reporting a 4 % improvement in both English and Spanish accuracy, and to our knowledge the first results for four other languages. 1