This paper describes the system used by the LIPN-IIMAS team in the Task 2, Semantic Textual Similarity, at SemEval 2015, in both the English and Spanish sub-tasks. We included some features based on alignment measures and we tested different learning models, in particular Random Forests, which proved the best among those used in our participation.

Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two texts. This paper presents the results of the STS pilot task in Semeval. The training data contained 2000 sentence pairs from previously existing paraphrase datasets and machine translation evaluation resources. The test data also comprised 2000 sentences pairs for those datasets, plus two surprise dataset...

Semantic Textual Similarity (STS) is a foundational NLP task and can be used in a wide range of tasks. To determine the STS of two texts, hundreds of different STS systems exist, however, for an NLP system designer, it is hard to decide which system is the best one. To answer this question, an intrinsic evaluation of the STS systems is conducted by comparing the output of the system to human ju...

Three experiments are reported showing that diagnosis of skin disorders by medical residents and general practitioners was facilitated by similar cases previously seen in the same context. Diagnosis of similar cases was facilitated more than that of dissimilar cases in the same diagnostic category, demonstrating that facilitation was not solely due to activation of the diagnostic category as a ...

This paper describes MITRE’s participation in the Semantic Textual Similarity task (SemEval-2017 Task 1), which evaluated machine learning approaches to the identification of similar meaning among text snippets in English, Arabic, Spanish, and Turkish. We detail the techniques we explored, ranging from simple bag-ofngrams classifiers to neural architectures with varied attention and alignment m...

We present results on the relation discovery task, which addresses some of the shortcomings of supervised relation extraction by applying minimally supervised methods. We describe a detailed experimental design that compares various configurations of conceptual representations and similarity measures across six different subsets of the ACE relation extraction data. Previous work on relation dis...

This paper introduces a new SemEval task on Cross-Level Semantic Similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Highquality data sets were constructed for four comparison types using multi-stage annotation procedures with a graded scale of similarity. Nineteen teams submitted ...

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How verbal and nonverbal visuoperceptual input connects to semantic knowledge is a core question in visual and cognitive neuroscience, with significant clinical ramifications. In an event-related functional magnetic resonance imaging (fMRI) experiment we determined how cosine similarity between fMRI response patterns to concrete words and pictures reflects semantic clustering and semantic dista...