Entity Linking is to link a name string from plain-text documents to the corresponding entry in given knowledge base. In this paper we demonstrate our entity linking system for TAC KBP 2011 Track. Our system implements pairwise and listwise learning to rank methods to create a ranking list of candidates with several kinds of features, including context similarity, term frequency, key entity ext...

Many Entity Linking systems use collective graph-based methods to disambiguate the entity mentions within a document. Most of them have focused on graph construction and initial weighting of the candidate entities, less attention has been devoted to compare the graph ranking algorithms. In this work, we focus on the graph-based ranking algorithms, therefore we propose to apply five centrality m...

Our method to Knowledge Base Population at TAC2012 is described in this paper. An enhanced pattern bootstrapping system is mainly utilized in the Slot Filling task. And for the Entity Linking task, query expansion method, rule-based method and entity similarity ranking strategy are combined.

Searching for named entities has been the subject of many researches in information retrieval. Our goal in participating in TREC 2010 Entity Ranking track is to look for reconizing any named entity in arbitrary categories and use this to rank candidate named entities. We propose to address the issue by means of a web oriented language modeling approach.

Our method to Knowledge Base Population 2013 is described in this paper. An pattern bootstrapping system with automatic pattern evaluation is mainly utilized in the Slot Filling task. For the Entity Linking task, query expansion method and entity similarity ranking strategy are mainly considered. And for sentiment holder and target detection in the Sentiment Slot Filling task, two CRF models ar...

The Web has not only grown in size, but also changed its character, due to collaborative content creation and an increasing amount of structure. Current Search Engines find Web pages rather than information or knowledge, and leave it to the searchers to locate the sought information within the Web page. A considerable fraction of Web searches contains named entities. We focus on how the Wikiped...

As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different en...

This paper gives an overview of our work done for the TREC 2009 Entity track. We propose a hierarchical relevance retrieval model for entity ranking. In this model, three levels of relevance are examined which are document, passage and entity, respectively. The final ranking score is a linear combination of the relevance scores from the three levels. Furthermore, we exploit the structure of tab...

This article proposes a novel framework for representing and measuring local coherence. Central to this approach is the entity-grid representation of discourse, which captures patterns of entity distribution in a text. The algorithm introduced in the article automatically abstracts a text into a set of entity transition sequences and records distributional, syntactic, and referential informatio...

In this paper, we present our system called LADS, tailored to work on the TREC Entity Track Task of Related Entity Finding. The LADS system consists of four key components: document retrieval, entity extraction, feature extraction and entity ranking. We adopt the open advancement framework for the rapid development and use a learning-to-rank approach to rank candidate entities. We also experime...