53,143 research outputs found
An Empirical Approach to Temporal Reference Resolution
This paper presents the results of an empirical investigation of temporal
reference resolution in scheduling dialogs. The algorithm adopted is primarily
a linear-recency based approach that does not include a model of global focus.
A fully automatic system has been developed and evaluated on unseen test data
with good results. This paper presents the results of an intercoder reliability
study, a model of temporal reference resolution that supports linear recency
and has very good coverage, the results of the system evaluated on unseen test
data, and a detailed analysis of the dialogs assessing the viability of the
approach.Comment: 13 pages, latex using aclap.st
Learning Sentence-internal Temporal Relations
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 temporal marker achieve good
performance on a pseudo-disambiguation task simulating temporal inference
(during testing the temporal marker is treated as unseen and the models must
select the right marker from a set of possible candidates). Secondly, we assess
whether the proposed approach holds promise for the semi-automatic creation of
temporal annotations. Specifically, we use a model trained on noisy and
approximate data (i.e., main and subordinate clauses) to predict
intra-sentential relations present in TimeBank, a corpus annotated rich
temporal information. Our experiments compare and contrast several
probabilistic models differing in their feature space, linguistic assumptions
and data requirements. We evaluate performance against gold standard corpora
and also against human subjects
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