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This paper describes the two systems for determining the semantic similarity of short texts submitted to the SemEval 2012 Task 6. Most of the research on semantic similarity of textual content focuses on large documents. However, a fair amount of information is condensed into short text snippets such as social media posts, image captions, and scientific(More)
In online discussions, users often back up their stance with arguments. Their arguments are often vague, implicit, and poorly worded, yet they provide valuable insights into reasons underpinning users' opinions. In this paper, we make a first step towards argument-based opinion mining from on-line discussions and introduce a new task of argument(More)
Derivational models are still an under-researched area in computational morphology. Even for German, a rather resource-rich language, there is a lack of large-coverage derivational knowledge. This paper describes a rule-based framework for inducing derivational families (i.e., clusters of lemmas in derivational relationships) and its application to create a(More)
Online debates sparkle argumentative discussions from which generally accepted arguments often emerge. We consider the task of unsupervised identification of prominent argument in online debates. As a first step, in this paper we perform a cluster analysis using semantic textual similarity to detect similar arguments. We perform a preliminary cluster(More)
Identifying synonyms is important for many natural language processing and information retrieval applications. In this paper we address the task of automatically identifying synonyms in Croatian language using distributional semantic models (DSM). We build several DSMs using latent semantic analysis (LSA) and random indexing (RI) on the large hrWaC corpus.(More)
Supervised word sense disambiguation (WSD) has been shown to achieve state-of-the-art results but at high annotation costs. Active learning can ameliorate that problem by allowing the model to dynamically choose the most informative word contexts for manual labeling. In this paper we investigate the use of active learning for Croa-tian WSD. We adopt a(More)