In this paper we present MisNet, a novel model for word level metaphor detection. MisNet converts two linguistic rules, i.e., Metaphor Identification Procedure (MIP) and Selectional Preference Violation (SPV) into semantic matching tasks. MIP module computes the similarity between the contextual meaning and the basic meaning of a target word. SPV module perceives the incongruity between target words and their contexts. To better represent basic meanings, MisNet utilizes dictionary resources. Empirical results indicate that MisNet achieves competitive performance on several datasets.

Estimated Time of Arrival (ETA) plays a vital role in many application scenarios. For example, in various scenarios such as online car-hailing order distribution, price estimation, mid-trip estimation, and route decision-making. Accurate arrival time estimation can help the platform improve efficiency. However, accurate arrival time estimation is affected by static information and dynamic information, and the estimated arrival time has high technical difficulties and challenges.