Artificial Intelligence (AI) has huge impact on our daily lives with applications such as voice assistants, facial recognition, chatbots, autonomously driving cars, etc. Natural Language Processing (NLP) is a cross-discipline of AI and Linguistics, dedicated to study the understanding of the text. This is a very challenging area due to unstructured nature of the language, with many ambiguous and corner cases. In this thesis we address a very specific area of NLP that involves the understanding of entities (e.g., names of people, organizations, locations) in text. First, we introduce a radically different, entity-centric view of the information in text. We argue that instead of using individual mentions in text to understand their meaning, we should build applications that would work in terms of entity concepts. Next, we present a more detailed model on how the entity-centric approach can be used for the entity linking task. In our work, we show that this task can be improved by considering performing entity linking at the coreference cluster level rather than each of the mentions individually. In our next work, we further study how information from Knowledge Base entities can be integrated into text. Finally, we analyze the evolution of the entities from the evolving temporal perspective.

翻译：人工智能（AI）已深刻影响我们的日常生活，如语音助手、人脸识别、聊天机器人、自动驾驶汽车等应用。自然语言处理（NLP）是AI与语言学的交叉学科，致力于研究对文本的理解。由于语言的非结构化特性以及诸多歧义与边缘案例，这是一个极具挑战性的领域。本论文聚焦NLP中一个特定方向，即理解文本中的实体（例如人名、组织名、地名）。首先，我们引入一种全新的、以实体为中心的文本信息视角。我们主张，不应通过文本中的个体指称来理解其含义，而应构建基于实体概念运作的应用系统。接着，我们提出一个更详细的模型，阐明如何将实体中心方法应用于实体链接任务。我们的研究表明，通过在共指消解簇层面（而非单个指称层面）执行实体链接，可以显著提升该任务的性能。后续工作进一步探讨如何将知识库中的实体信息整合到文本中。最后，我们从时间演化的动态视角剖析实体的演变过程。