Toponym extraction and disambiguation enhancement using loops of feedback Toponym extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. This paper addresses two problems with toponym extraction and disambiguation. First, almost no existing works examine the extraction and disambiguation interdependency. Second, existing disambiguation techniques mostly take as input extracted named entities without considering the uncertainty and imperfection of the extraction process. In this paper we aim to investigate both avenues and to show that explicit handling of the uncertainty of annotation has much potential for making both extraction and disambiguation more robust. We conducted experiments with a set of holiday home descriptions with the aim to extract and disambiguate toponyms. We show that the extraction confidence probabilities are useful in enhancing the effectiveness of disambiguation. Reciprocally, retraining the extraction models with information automatically derived from the disambiguation results, improves the extraction models. This mutual reinforcement is shown to even have an effect after several automatic iterations.

DSEL: A DomainSpecific Entity Linking System Entity linking refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB). It can bridge the gap between unstructured nature language documents that computers hardly understand and a structured semantic Knowledge Base which can be easily processed by computers. Existing studies and systems about entity linking mainly focus on the open domain, which may cause three problems: 1. linking to unconcerned entities; 2. time and space consuming; 3. less precision. In this paper, we address the problem by restricting entity linking into specific domains and leveraging domain information to enhance the linking performance. We propose an unsupervised method to generate domain data from Wikipedia and provide a domainspecific neural collective entity linking model for each domain. Based on domain data and domain models, we build a system that can provide domain entity linking for users. Our system, DomainSpecific neural collective Entity Linking system (DSEL), supporting entity linking in 12 domains, is published as an online website, https:dsel.xlore.org.