We used the memory-based learner Timbl (Daelemans et al., 2002) to find names in English and German newspaper text. A first system used only the training data, and a number of gazetteers. The results show that gazetteers are not beneficial in the English case, while they are for the German data. Type-token generalization was applied, but also reduced performance. The second system used gazettee...

Named entities in topics are a major factor contributing to the quality of retrieval results. In this paper, we report on an analysis on the correlation between the number of named entities present in a topic and the retrieval quality achieved for these topics by retrieval systems within CLEF. We found that a medium positive correlation exists for German, English and Spanish topics. Furthermore...

We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as the representation of text chunks, the inference approach needed to combine local NER decisions, the sources of prior knowledge and how to use them within an NER system. In the process of comparing several solutions...

This report describes the methods and results of a system developed for the GREC Named Entity Recognition and GREC Named Entity Regeneration Challenges 2010. We explain our process of automatically annotating surface text, as well as how we use this output to select improved referring expressions for named entities.

This paper discusses the participation of the University of Alicante in the RTE track at TAC 2009. The proposed system faces the entailment recognition by computing shallow lexical deductions and richer inferences based on semantics. Specifically on WordNet, detection of negation terms, named entity recognition, verbs implications and frame semantic analysis. Although the system was designed to...

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...