background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-the-art machine learning methods, combined with some knowledge-based features in hy-brid systems.
In this paper, we investigate a semi-supervised learning approach based on neural networks for neste...
This paper presents the BECREATIVE Named Entity Recognition system and its participation at the Germ...
In this paper, we investigate a semi-supervised learning approach based on neural networks for neste...
This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVEN...
Collobert et al. (2011) showed that deep neural network architectures achieve state- of-the-art perf...
Named entity recognition (NER) is of vital importance in information extraction in natural language ...
Machine Learning is described in today’s Information Technology world as one of the most promising r...
Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as pro...
In this paper, we investigate a semi-supervised learning approach based on neu-ral networks for nest...
The survey of research in the field of Named Entity Recognition and Classification (NERC) features, ...
Named entity recognition (NER) is a subfield of information extraction, which aims to detect and cla...
Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific s...
Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific s...
Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific s...
In this paper, we investigate a semi- supervised learning approach based on neu- ral networks for ne...
In this paper, we investigate a semi-supervised learning approach based on neural networks for neste...
This paper presents the BECREATIVE Named Entity Recognition system and its participation at the Germ...
In this paper, we investigate a semi-supervised learning approach based on neural networks for neste...
This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVEN...
Collobert et al. (2011) showed that deep neural network architectures achieve state- of-the-art perf...
Named entity recognition (NER) is of vital importance in information extraction in natural language ...
Machine Learning is described in today’s Information Technology world as one of the most promising r...
Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as pro...
In this paper, we investigate a semi-supervised learning approach based on neu-ral networks for nest...
The survey of research in the field of Named Entity Recognition and Classification (NERC) features, ...
Named entity recognition (NER) is a subfield of information extraction, which aims to detect and cla...
Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific s...
Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific s...
Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific s...
In this paper, we investigate a semi- supervised learning approach based on neu- ral networks for ne...
In this paper, we investigate a semi-supervised learning approach based on neural networks for neste...
This paper presents the BECREATIVE Named Entity Recognition system and its participation at the Germ...
In this paper, we investigate a semi-supervised learning approach based on neural networks for neste...