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	title        = {The SweLL Language Learner Corpus: From Design to Annotation},
	abstract     = {The article presents a new language learner corpus for Swedish, SweLL, and the methodology from collection and pesudonymisation to protect personal information of learners to annotation adapted to second language learning. The main aim is to deliver a well-annotated corpus of essays written by second language learners of Swedish and make it available for research through a browsable environment. To that end, a new annotation tool and a new project management tool have been implemented, – both with the main purpose to ensure reliability and quality of the final corpus. In the article we discuss reasoning behind metadata selection, principles of gold corpus compilation and argue for separation of normalization from correction annotation.},
	journal      = {Northern European Journal of Language Technology},
	author       = {Volodina, Elena and Granstedt, Lena and Matsson, Arild and Megyesi, Beáta and Pilán , Ildikó  and Prentice, Julia and Rosén, Dan and Rudebeck, Lisa  and Schenström, Carl-Johan and Sundberg, Gunlög and Wirén, Mats},
	year         = {2019},
	volume       = {6},
	pages        = {67--104},

	title        = {SVALA: an Annotation Tool for Learner Corpora generating parallel texts},
	abstract     = {Learner corpora are actively used for research on Language Acquisition and in Learner Corpus Research (LCR).  The  data  is,  however,  very  expensive  to  collect  and  manually  annotate,  and  includes  steps  like  anonymization,  normalization, error annotation, linguistic annotation. In the past, projects often re - used tools from a number of  different projects for the above steps. As a result, various input and output formats between the tools needed to  be converted, which increased the complexity of the task. In  the  present  project,  we  are  developing  a  tool  that  handles  all  of  the  above - mentioned  steps  in  one  environment maintaining a stable interpretable  format between the  steps. A distinguishing feature of the tool is  that users work in a usual environment (plain text) while the tool visualizes all performed edits via a graph that  links an original learner text with an edited one, token by token.},
	booktitle    = {Learner Corpus Research conference (LCR-2019), Warsaw, 12-14 September 2019, Book of abstracts},
	author       = {Volodina, Elena and Matsson, Arild and Rosén, Dan and Wirén, Mats },
	year         = {2019},

	title        = {ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering},
	abstract     = {We present ImageTTR, an extension to the Python implementation of Type Theory with Records (pyTTR) which connects formal record type representation with image classifiers implemented as deep neural networks. The Type Theory with Records framework serves as a knowledge representation system for natural language the representations of which are grounded in perceptual information of neural networks. We demonstrate the benefits of this symbolic and data-driven hybrid approach on the task of visual question answering.},
	booktitle    = {Proceedings of the IWCS 2019 Workshop on Computing Semantics with Types, Frames and Related Structures, May 24, 2019, Gothenburg, Sweden / Rainer Osswald, Christian Retoré, Peter Sutton (Editors)},
	author       = {Matsson, Arild and Dobnik, Simon and Larsson, Staffan},
	year         = {2019},
	publisher    = {Association for Computational Linguistics},
	address      = {Stroudsburg, PA },
	ISBN         = {978-1-950737-25-3},