Software chatbots are growing in popularity and have been increasingly used in software projects due to their benefits in saving time, cost, and effort. At the core of every chatbot is a Natural Language Understanding (NLU) component that enables chatbots to comprehend the users’ queries. Prior work shows that chatbot practitioners face challenges in training the NLUs because the labeled training data is scarce. Consequently, practitioners resort to user queries to enhance chatbot performance. They annotate these queries and use them for NLU training. However, such training is done manually and prohibitively expensive. Therefore, we propose AlphaBot to automate the query annotation process for SE chatbots. Specifically, we leverage weak supervision to label users’ queries posted to a software repository-based chatbot. To evaluate the impact of using AlphaBot on the NLU’s performance, we conducted a case study using a dataset that comprises 749 queries and 52 intents. The results show that using AlphaBot improves the NLU’s performance in terms of F1-score, with improvements ranging from 0.96% to 35%. Furthermore, our results show that applying more labeling functions improves the NLU’s classification of users’ queries. Our work enables practitioners to focus on their chatbots’ core functionalities rather than annotating users’ queries.