198 research outputs found
GermEval 2014 Named Entity Recognition Shared Task: Companion Paper
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, 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 hybrid systems
GermEval 2014 Named Entity Recognition Shared Task: Companion Paper
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, 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 hybrid systems
Unsupervised Sense-Aware Hypernymy Extraction
In this paper, we show how unsupervised sense representations can be used to
improve hypernymy extraction. We present a method for extracting disambiguated
hypernymy relationships that propagates hypernyms to sets of synonyms
(synsets), constructs embeddings for these sets, and establishes sense-aware
relationships between matching synsets. Evaluation on two gold standard
datasets for English and Russian shows that the method successfully recognizes
hypernymy relationships that cannot be found with standard Hearst patterns and
Wiktionary datasets for the respective languages.Comment: In Proceedings of the 14th Conference on Natural Language Processing
(KONVENS 2018). Vienna, Austri
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