Learning the meaning of a verb is challenging because learners need to resolve two types of ambiguity: (1) word-referent mapping--finding the correct referent event of a verb, and (2) word-meaning mapping--inferring the correct meaning of the verb from the referent event (e.g., whether the meaning of an action word is TURNING or TWISTING). The present work examines how adult learners solve this challenge by utilizing both in-the-moment linguistic information within individual learning situations and cross-situational statistical information across multiple learning situations. We investigate how different cues provided in the moment affect information selection and how cross-situational learning as a general computational mechanism allows for information integration over time. Two experiments were designed based on a Human Simulation Paradigm, in which adult learners were presented with a sequence of short videos from parent-toddler toy play and asked to guess a mystery verb the parent produced in each video. In Experiment 1, we compared individual learning situations containing linguistic information to the exact same learning scenes without linguistic information and found that linguistic information helped learners narrow down the meaning of a verb embedded in individual situations, which was consistent with prior research. In Experiment 2, the videos sharing the same target verb were presented in a blocked design to incorporate cross-situational statistics for the same verb. We measured the variability, convergence, and accuracy of participants' guesses. Within-trial linguistic information allowed learners to quickly narrow down their search space and focus on a few relevant aspects in a scene, while cross-situational learning allowed them to fine-tune their learning further across trials. Our findings support a unified account wherein within-trial linguistic information and cross-situational statistical information are integrated for more efficient verb learning.