User intent understanding is a crucial step in designing both conversational
agents and search engines. Detecting or inferring user intent is challenging,
since the user utterances or queries can be short, ambiguous, and contextually
dependent. To address these research challenges, my thesis work focuses on: 1)
Utterance topic and intent classification for conversational agents 2) Query
intent mining and classification for Web search engines, focusing on the
e-commerce domain. To address the first topic, I proposed novel models to
incorporate entity information and conversation-context clues to predict both
topic and intent of the user's utterances. For the second research topic, I
plan to extend the existing state of the art methods in Web search intent
prediction to the e-commerce domain, via: 1) Developing a joint learning model
to predict search queries' intents and the product categories associated with
them, 2) Discovering new hidden users' intents. All the models will be
evaluated on the real queries available from a major e-commerce site search
engine. The results from these studies can be leveraged to improve performance
of various tasks such as natural language understanding, query scoping, query
suggestion, and ranking, resulting in an enriched user experience.

研究如何通过建立新模型来识别用户意图，以提高自然语言处理、查询分析、查询建议和排序等搜索引擎任务的效果。