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Existing literature demonstrates that verbs are pivotal in legal information extraction tasks due to their semantic and argumentative properties. However, granting computers the ability to interpret the meaning of a verb and its semantic properties in relation to a given context can be considered as a challenging task, mainly due to the polysemic and domain specific behaviours of verbs. Therefore, developing mechanisms to identify behaviors of verbs and evaluate how artificial models detect the domain specific and polysemic behaviours of verbs can be considered as tasks with significant importance. In this regard, a comprehensive dataset that can be used as an evaluation resource, as well as a training data set, can be considered as a major requirement. In this paper, we introduce LeCoVe, which is a verb similarity dataset intended towards facilitating the process of identifying verbs with similar meanings in a legal domain specific context. Using the dataset, we evaluated both domain specific and domain generic embedding models, which were developed using state-of-the-art word representation and language modelling techniques. As a part of the experiments carried out using the announced dataset, Sense2Vec and BERT models were trained using a corpus of legal opinion texts in order to capture domain specific behaviours. In addition to LeCoVe, we demonstrate that a neural network model, which was developed by combining semantic, syntactic, and contextual features that can be obtained from the outputs of embedding models, can perform comparatively well, even in a low resource scenario. [ABSTRACT FROM AUTHOR]