This paper presents the UNITOR system that participated in the ~*SEM 2013 shared task on Semantic Textual Similarity (STS). The task is modeled as a Support Vector (SV) regression problem, where a similarity scoring function between text pairs is acquired from examples. The proposed approach has been implemented in a system that aims at providing high applicability and robustness, in order to reduce the risk of over-fitting over a specific datasets. Moreover, the approach does not require any manually coded resource (e.g. WordNet), but mainly exploits distributional analysis of un-labeled corpora. A good level of accuracy is achieved over the shared task: in the Typed STS task the proposed system ranks in 1st and 2nd position.
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机译：本文介绍了UNITOR系统，该系统参与了〜* SEM 2013语义文本相似性（STS）共享任务。该任务被建模为支持向量（SV）回归问题，其中从示例中获取文本对之间的相似性评分功能。所提出的方法已在旨在提供高适用性和鲁棒性的系统中实施，以减少对特定数据集进行过度拟合的风险。而且，该方法不需要任何手动编码的资源（例如，WordNet），而是主要利用对未标记的语料库的分布分析。在共享任务上可以达到较高的准确性：在Typed STS任务中，建议的系统位于第一和第二位置。
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