We propose a method for linking entities in a stream of short textual documents that takes into account context both inside a document and inside the history of documents seen so far. Our method uses a generic optimization framework for combining several entity ranking functions, and we introduce a global control function to control optimization. Our results demonstrate the effectiveness of com...

Due to the huge amount of data published on the Web, the Web search process has become more difficult, and it is sometimes hard to get the expected results, especially when the users are less certain about their information needs. Several efforts have been proposed to support exploratory search on the web by using query expansion, faceted search, or supplementary information extracted from exte...

Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there is a large body of work on entity linking in documents, entity linking in queries poses new challenges due to the limited context the query provides coupled with the efficiency requirements of an online setting. Our goal is to gain a deeper understanding of how to ...

This paper describes experiments carried out at NiCT for the TREC 2009 Entity Ranking track. Our main study is to develop an effective approach to rank entities via measuring the “similarities” between supporting snippets of entities and input query. Three models are implemented to this end. 1) The DLM regards entity ranking as a task of calculating the probabilities of generating input query g...

This paper overviews BUPTTeam’s participation in the Entity Linking task at TAC 2012. In this paper we propose a method to link the queries and KB entries based on four steps: 1) query expansion, 2) candidates generation, 3) candidates ranking, 4) clustering. In the initial stage, we expand the queries from the background documents using the defined rules. We generate in the knowledge base acco...

Recent years have witnessed some convergence in the architecture of entity search systems driven by a knowledge graph (KG) and a corpus with annotated entity mentions. However, each specific system has some limitations. We present AQQUCN, an entity search system that combines the best design principles into a public reference implementation. AQQUCN does not depend on well-formed question syntax...

We describe a task-sensitive approach to retrieval and ranking of semantic entities, using the domain information available in an enterprise. Our approach utilizes noisy namedentity tagging and document classification, on top of an enterprise search engine, to provide input to a novel ranking metric for each entity retrieved for a task. Retrieval is query-centric, where the user query is the ta...

In this paper, we present a community answers ranking system which is based on Grice Maxims. In particular, we describe a ranking system which is based on answer relevancy scores, assigned by three main components: Named entity recognition, similarity score, and sentiment analysis.

In this paper, we present the SemLinker system used by the polymtl team in the English entity linking track of TAC-KBP 2013. To improve the disambiguation process, SemLinker re-uses and enriches the entity links provided by a generic annotation engine. The linking is done through a re-ranking process on the candidate links associated with a given named entity. This process relies on the mutual ...

Entity is an important information carrier in Web pages. Searchers often want a ranked list of relevant entities directly rather a list of documents. So the research of related entity finding (REF) is a meaningful work. In this paper we investigate the most important task of REF: Entity Ranking. To address the issue of wrong entity type in entity ranking: some retrieved entities don’t belong to...