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 …

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 …

Temporal taggers are usually developed for a certain language. Besides English, only few
languages have been addressed, and only the temporal tagger HeidelTime covers several …

Time information plays an important role in the areas of data mining, information retrieval,
and natural language processing. Among the linguistic tasks related to time expressions …

Temporal expression identification is crucial for understanding texts written in natural
language. Although highly effective systems such as HeidelTime exist, their limited runtime …

The lack of publicly accessible text corpora is a major obstacle for progress in natural
language processing. For medical applications, unfortunately, all language communities …

Based on the theoretical foundations laid out in the previous chapters, we now explain the
challenges of temporal tagging. As most of the research on temporal tagging so far dealt …

We describe the creation of GRASCCO, a novel German-language corpus composed of
some 60 clinical documents with more than. 43,000 tokens. GRASCCO is a synthetic corpus …

The detection and normalization of temporal expressions is an important task and
preprocessing step for many applications. However, prior work on normalization is rule …

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 …