Refine
Year of publication
- 2014 (2)
Document Type
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- Computerlinguistik (1)
- Digital Humanities (1)
- NER (1)
- Named entity recognition (1)
Institute
In this paper, we investigate a semi- supervised learning approach based on neu- ral networks for nested named entity recog- nition on the GermEval 2014 dataset. The dataset consists of triples of a word, a named entity associated with that word in the first-level and one in the second-level. Additionally, the tag distribution is highly skewed, that is, the number of occurrences of certain types of tags is too small. Hence, we present a unified neural network archi- tecture to deal with named entities in both levels simultaneously and to improve gen- eralization performance on the classes that have a small number of labelled examples.
Digital libraries allow us to organize a vast amount of publications in a structured way and to extract information of user’s interest. In order to support customized use of digital libraries, we develop novel methods and techniques in the Knowledge Discovery in Scientific Literature (KDSL) research program of our graduate school. It comprises several sub-projects to handle specific problems in their own fields. The sub-projects are tightly connected by sharing expertise to arrive at an integrated system. To make consistent progress towards enriching digital libraries to aid users by automatic search and analysis engines, all methods developed in the program are applied to the same set of freely available scientific articles.
