Extraction and normalization of temporal expressions from documents are important steps
towards deep text understanding and a prerequisite for many NLP tasks such as information …

We explore a semi-supervised approach for improving the portability of time expression
recognition to non-newswire domains: we generate additional training examples by …

In the last years, temporal tagging has received increasing attention in the area of natural
language processing. However, most of the research so far concentrated on processing …

Interpreting Motion presents an integrated perspective on how language structures constrain
concepts of motion and how the world shapes the way motion is linguistically expressed …

The reliable extraction of knowledge from text requires an appropriate treatment of the time
at which reported events take place. Unfortunately, there are very few annotated data sets …

Objectives To test the feasibility of using text mining to depict meaningfully the experience of
pain in patients with metastatic prostate cancer, to identify novel pain phenotypes, and to …

Temporal expressions are words or phrases that describe a point, duration or recurrence in
time. Automatically annotating these expressions is a research goal of increasing interest …

Time is deeply woven into how people perceive, and communicate about the world. Almost
unconsciously, we provide our language utterances with temporal cues, like verb tenses …

A new architecture for identifying and interpreting temporal expressions is introduced, in
which the large set of complex hand-crafted rules standard in systems for this task is …

Most of the research on temporal tagging so far is done for processing English text
documents. There are hardly any multilingual temporal taggers supporting more than two …