@article{NevesLeser2015,
  author    = {Neves, Mariana and Leser, Ulf},
  title     = {Question answering for Biology},
  series = {Methods : focusing on rapidly developing techniques},
  volume    = {74},
  journal   = {Methods : focusing on rapidly developing techniques},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {1046-2023},
  doi       = {10.1016/j.ymeth.2014.10.023},
  pages     = {36 -- 46},
  year      = {2015},
  abstract  = {Biologists often pose queries to search engines and biological databases to obtain answers related to ongoing experiments. This is known to be a time consuming, and sometimes frustrating, task in which more than one query is posed and many databases are consulted to come to possible answers for a single fact. Question answering comes as an alternative to this process by allowing queries to be posed as questions, by integrating various resources of different nature and by returning an exact answer to the user. We have surveyed the current solutions on question answering for Biology, present an overview on the methods which are usually employed and give insights on how to boost performance of systems in this domain. (C) 2014 Elsevier Inc. All rights reserved.},
  language  = {en}
}
@article{ChiarcosDipperGoetzeetal.2008,
  author    = {Chiarcos, Christian and Dipper, Stefanie and G{\"o}tze, Michael and Leser, Ulf and L{\"u}deling, Anke and Ritz, Julia and Stede, Manfred},
  title     = {A flexible framework for integrating annotations from different tools and tag sets},
  issn      = {1248-9433},
  year      = {2008},
  abstract  = {We present a general framework for integrating annotations from different tools and tag sets. When annotating corpora at multiple linguistic levels, annotators may use different expert tools for different phenomena or types of annotation. These tools employ different data models and accompanying approaches to visualization, and they produce different output formats. For the purposes of uniformly processing these outputs, we developed a pivot format called PAULA, along with converters to and from tool formats. Different annotations are not only integrated at the level of data format, but are also joined on the level of conceptual representation. For this purpose, we introduce OLiA, an ontology of linguistic annotations that mediates between alternative tag sets that cover the same class of linguistic phenomena. All components are integrated in the linguistic information system ANNIS : Annotation tool output is converted to the pivot format PAULA and read into a database where the data can be visualized, queried, and evaluated across multiple layers. For cross-tag set querying and statistical evaluation, ANNIS uses the ontology of linguistic annotations. Finally, ANNIS is also tied to a machine learning component for semiautomatic annotation.},
  language  = {en}
}