text: We present an LDA approach to entity disambiguation. Each topic is associated with a Wikipedia article and topics generate either content words or entity mentions. Training such models is challenging because of the topic and vocabulary size, both in the millions. We tackle these problems using a novel distributed inference and representation framework based on a parallel Gibbs sampler guided by the Wikipedia link graph, and pipelines of MapReduce allowing fast and memoryfrugal processing of large datasets. We report state-of-the-art performance on a public dataset.
  @cite_27: The primary purpose of news articles is to convey information about who, what, when and where. But learning and summarizing these relationships for collections of thousands to millions of articles is difficult. While statistical topic models have been highly successful at topically summarizing huge collections of text documents, they do not explicitly address the textual interactions between who where, i.e. named entities (persons, organizations, locations) and what, i.e. the topics. We present new graphical models that directly learn the relationship between topics discussed in news articles and entities mentioned in each article. We show how these entity-topic models, through a better understanding of the entity-topic relationships, are better at making predictions about entities.
  @cite_14: Disambiguating entity references by annotating them with unique ids from a catalog is a critical step in the enrichment of unstructured content. In this paper, we show that topic models, such as Latent Dirichlet Allocation (LDA) and its hierarchical variants, form a natural class of models for learning accurate entity disambiguation models from crowd-sourced knowledge bases such as Wikipedia. Our main contribution is a semi-supervised hierarchical model called Wikipedia-based Pachinko Allocation Model (WPAM) that exploits: (1) All words in the Wikipedia corpus to learn word-entity associations (unlike existing approaches that only use words in a small fixed window around annotated entity references in Wikipedia pages), (2) Wikipedia annotations to appropriately bias the assignment of entity labels to annotated (and co-occurring unannotated) words during model learning, and (3) Wikipedia's category hierarchy to capture co-occurrence patterns among entities. We also propose a scheme for pruning spurious nodes from Wikipedia's crowd-sourced category hierarchy. In our experiments with multiple real-life datasets, we show that WPAM outperforms state-of-the-art baselines by as much as 16 in terms of disambiguation accuracy.
  @cite_4: Entity Linking (EL) has received considerable attention in recent years. Given many name mentions in a document, the goal of EL is to predict their referent entities in a knowledge base. Traditionally, there have been two distinct directions of EL research: one focusing on the effects of mention's context compatibility, assuming that "the referent entity of a mention is reflected by its context"; the other dealing with the effects of document's topic coherence, assuming that "a mention's referent entity should be coherent with the document's main topics". In this paper, we propose a generative model -- called entity-topic model, to effectively join the above two complementary directions together. By jointly modeling and exploiting the context compatibility, the topic coherence and the correlation between them, our model can accurately link all mentions in a document using both the local information (including the words and the mentions in a document) and the global knowledge (including the topic knowledge, the entity context knowledge and the entity name knowledge). Experimental results demonstrate the effectiveness of the proposed model.
summary: A few topic model-inspired approaches have been proposed for modeling entities @cite_27 @cite_14 @cite_4 . Early work @cite_27 presents extensions to LDA to model both words and entities; however, they treat entities as , not linked to a knowledge base. @cite_14 @cite_4 model a document as a collection of topic mentions, materializing as words or phrases, with topics being identified with Wikipedia articles. Kataria in particular investigate the use of the Wikipedia category graph as the topology of a hierarchical topic model. The main drawback of this proposal is its scalability both in terms of efficiency and topic coverage; they prune Wikipedia to a subset of approximately 60k entities, reporting training times of 2.6 days. Han & Sun carried out the largest experiment of this kind, training on 3M Wikipedia documents (and no graph), reporting training times of one week with a memory footprint of 20GB on one machine. Our goal is to provide full Wikipedia coverage and high annotation accuracy with reasonable training processing efficiency.