We propose representing a text corpus as a labeled directed graph, where nodes represent words and weighted edges represent the syntactic relations between them, as derived by dependency parsing. Given this graph, we adopt a graph-based similarity measure based on random walks to derive a similarity measure between words, and also use supervised learning to improve the derived similarity measur...

Many NLP applications rely on the existence of similarity measures over text data. Although word vector space models provide good similarity measures between words, phrasal and sentential similarities derived from composition of individual words remain as a difficult problem. In this paper, we propose a new method of of non-linear similarity learning for semantic compositionality. In this metho...

We propose representing a text corpus as a labeled directed graph, where nodes represent words and weighted edges represent the syntactic relations between them, as derived by dependency parsing. Given this graph, we adopt a graph-based similarity measure based on random walks to derive a similarity measure between words, and also use supervised learning to improve the derived similarity measur...

Empirical success of kernel-based learning algorithms is very much dependent on the kernel function used. Instead of using a single fixed kernel function, multiple kernel learning (MKL) algorithms learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem. We study multitask learning (MTL) problems and formulate a novel M...

We present in this paper our system developed for SemEval 2015 Shared Task 2 (2a English Semantic Textual Similarity, STS, and 2c Interpretable Similarity) and the results of the submitted runs. For the English STS subtask, we used regression models combining a wide array of features including semantic similarity scores obtained from various methods. One of our runs achieved weighted mean corre...

Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...

Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM 2013 and SemEval-2014 tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines Latent Semantic Analysis and machine learning augmented with data from se...

Similarity is at the core of scientific inquiry in general and is one of the basic functionalities in Natural Language Processing (NLP) in particular. To arrive at generalizations across different phenomena, we need to recognize patterns of similarity, or divergence, to make scientific claims. Semantic textual similarity plays a significant role in NLP research both directly and indirectly. For...

A widely held but rarely tested assumption among cognitive scientists is that different cognitive tasks may rely upon a single basic cognitive process. Using an established methodology to examine the suppression and subsequent rebound of mental operations, the present research indicates that suppressing use of similarity in one domain results in the subsequent rebound of similarity assessment i...