This paper describes an approach at Named Entity Recognition (NER) in German language
documents from the legal domain. For this purpose, a dataset consisting of German court …

The evaluation of summaries is a challenging but crucial task of the summarization field. In
this work, we propose to learn an automatic scoring metric based on the human judgements …

We present a multilingual Named Entity Recognition approach based on a robust and
general set of features across languages and datasets. Our system combines shallow local …

We describe a dataset developed for Named Entity Recognition in German federal court
decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource …

Automatic metaphor detection usually relies on various features, incorporating eg
selectional preference violations or concreteness ratings to detect metaphors in text. These …

This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task
workshop at KONVENS. It provides background information on the motivation of this task …

We ask how to practically build a model for German named entity recognition (NER) that
performs at the state of the art for both contemporary and historical texts, ie, a big-data and a …

This paper introduces DaN+, a new multi-domain corpus and annotation guidelines for
Danish nested named entities (NEs) and lexical normalization to support research on cross …

In this paper, we investigate how deep learning techniques can be applied to discourse
pragmatics. As a testcase we analyse heuristic textual practices, defined as linguistic …

Extracting named entities text forms the basis for many crucial tasks such as information
retrieval and extraction, machine translation, opinion mining, sentiment analysis and …