Understanding the semantics of Web content is at the core of many applications, ranging from Web search, news aggregation and machine translation to personal assistant services such as Amazon Echo, Cortana, Siri, and Google Home. Presently, two different approaches apply to this task. The first approach utilizes a rich suite of information retrieval and machine learning techniques that capture meaning through powerful statistical tools like neural networks and distributional semantics (e.g., word2vec). Recently, such emerging semantic models have achieved state-of-the-art results in several predictive applications (e.g. recommendation, node classification, knowledge graph completion) relevant not just to the Semantic Web, but allied communities such as data mining and natural language processing. The second, more traditional, approach conveys meaning in a structured form through embedded data markup (using Schema.org, OGP, etc.) and ontologies, and can be further enhanced through available knowledge bases such as Freebase and DBpedia. Ontological notions of semantics play a central role in the Semantic Web community, permitting publishers and consumers of data to interact using a well-defined ontology. Such models give practitioners the powerful ability to reason about, and represent, instances in a well-defined manner. HSSUES is a half-day workshop that will explore the synergy, from perspectives of theory, application, experiments (including negative results) and vision, between both approaches, and how such synergies can be exploited to advance the state-of-the-art. We are interested in mechanisms, both theoretical and experimental, that range the spectrum of possible strategies and provide novel functionalities through hybrid approaches to statistical and symbolic understanding. The broader goal is to foster a discussion that will lead to cross-cutting ideas and collaborations at a timely moment when Semantic Web research has significantly started intersecting with the natural language processing and knowledge discovery communities.