This paper explores the use of unlabeled data in a knowledge-poor approach to German NER. German is especially interesting for NER since not only names but all nouns are capitalized. Therefore, large and reliable lexical resources are necessary to develop and adapt systems for NER. Motivated by a model of word form observance, distinguishing three levels of different granularity, a method for t...

Creating the annotated corpus for training a named entity recognition model is expensive, particularly in specialised domains, such as medicine, which require expert annotators. Moreover, a model trained on text from one medical sub-domain often shows a drop in performance when applied on texts from another sub-domain, and annotated text from this other sub-domain might be required. When incorp...

In the paper we present an adaptation of Liner2 framework to solve the BSNLP 2017 shared task on multilingual named entity recognition. The tool is tuned to recognize and lemmatize named entities for Polish.

Our contribution is centred on a study of Named Entity (NE) recognition on speech transcripts and how it impacts on the accuracy of the final question answering system. AnswerFinder was adapted to the task of question answering on speech transcripts and participated in the QAst pilot track of the CLEF competition. We have ported AFNER, the NE recogniser of AnswerFinder, to the set of answer typ...

The following four species (only females available) of the Philometridae (Nematoda: Dracunculoidea) were recorded from freshwater fishes of Lake Turkana, northwestern Kenya in 2007-2008: Philometra bagri (Khalil, 1965) from the subcutaneous tissue around the mouth, on gill covers and the fin base of the bayad Bagrus bajad (Forsskål) (Bagridae: Siluriformes), Philometra lati sp. n. from the abdo...