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	title        = {Issue Salience on Twitter During Swedish Party Leaders’ Debates },
	abstract     = {The objective of this study is to contribute knowledge about formation of political agendas on Twitter during mediated political events, using the party leaders’ debates in Sweden before the general election of 2014 as a case study. Our findings show that issues brought up during the debates were largely mirrored on Twitter, with one striking discrepancy. Contrary to our expectations, issues on the left-right policy dimension were more salient on Twitter than in the debates, whereas issues such as the environment, immigration and refugees, all tied to a liberal-authoritarian value axis, were less salient on Twitter.},
	journal      = {Nordicom Review},
	author       = {Sandberg, Linn and Bjereld, Ulf and Bunyik, Karina and Forsberg, Markus and Johansson, Richard},
	year         = {2019},
	volume       = {40},
	number       = {2},
	pages        = {49--61},

	title        = {Character-based Recurrent Neural Networks for Morphological Relational Reasoning},
	abstract     = {We present a model for predicting inflected word forms based on morphological analogies. Previous work includes rule-based algorithms that determine and copy affixes from one word to another, with limited support for varying inflectional patterns. In related tasks such as morphological reinflection, the algorithm is provided with an explicit enumeration of morphological features which may not be available in all cases. In contrast, our model is feature-free: instead of explicitly representing morphological features, the model is given a demo pair that implicitly specifies a morphological relation (such as write:writes specifying infinitive:present). Given this demo relation and a query word (e.g. watch), the model predicts the target word (e.g. watches). To address this task, we devise a character-based recurrent neural network architecture using three separate encoders and one decoder.

Our experimental evaluation on five different languages shows tha the exact form can be predicted with high accuracy, consistently beating the baseline methods. Particularly, for English the prediction accuracy is 95.60%. The solution is not limited to copying affixes from the demo relation, but generalizes to words with varying inflectional patterns, and can abstract away from the orthographic level to the level of morphological forms.},
	journal      = {Journal of Language Modeling},
	author       = {Mogren, Olof and Johansson, Richard},
	year         = {2019},
	volume       = {7},
	number       = {1},
	pages        = {93--124},

	title        = {Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from IMF Loan Agreements},
	abstract     = {Social science researchers often use text as the raw data in investigations: for instance, when investigating the effects of IMF policies on the development of countries under IMF programs, researchers typically encode structured descriptions of the programs using a time-consuming manual effort. Making this process automatic may open up new opportunities in scaling up such investigations. As a first step towards automatizing this coding process, we describe an experiment where we apply a sentence classifier that automatically detects mentions of policy conditions in IMF loan agreements and divides them into different types. The results show that the classifier is generally able to detect the policy conditions, although some types are hard to distinguish.},
	booktitle    = {Proceedings of the 22nd Nordic Conference on Computational Linguistics; September 30 – October 2; Turku, Finland},
	author       = {Åkerström, Joakim and Daoud, Adel and Johansson, Richard},
	year         = {2019},
	publisher    = {Linköping University Electronic Press},