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.
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.
@cite_27 @cite_14 @cite_4
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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. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. In this work, we address the problem of joint modeling of text and citations in the topic modeling framework. We present two different models called the Pairwise-Link-LDA and the Link-PLSA-LDA models. The Pairwise-Link-LDA model combines the ideas of LDA [4] and Mixed Membership Block Stochastic Models [1] and allows modeling arbitrary link structure. However, the model is computationally expensive, since it involves modeling the presence or absence of a citation (link) between every pair of documents. The second model solves this problem by assuming that the link structure is a bipartite graph. As the name indicates, Link-PLSA-LDA model combines the LDA and PLSA models into a single graphical model. Our experiments on a subset of Citeseer data show that both these models are able to predict unseen data better than the baseline model of Erosheva and Lafferty [8], by capturing the notion of topical similarity between the contents of the cited and citing documents. Our experiments on two different data sets on the link prediction task show that the Link-PLSA-LDA model performs the best on the citation prediction task, while also remaining highly scalable. In addition, we also present some interesting visualizations generated by each of the models. Cross-document coreference, the problem of resolving entity mentions across multi-document collections, is crucial to automated knowledge base construction and data mining tasks. However, the scarcity of large labeled data sets has hindered supervised machine learning research for this task. In this paper we develop and demonstrate an approach based on distantly-labeling'' a data set from which we can train a discriminative cross-document coreference model. In particular we build a dataset of more than a million people mentions extracted from 3.5 years of New York Times articles, leverage Wikipedia for distant labeling with a generative model (and measure the reliability of such labeling); then we train and evaluate a conditional random field coreference model that has factors on cross-document entities as well as mention-pairs. This coreference model obtains high accuracy in resolving mentions and entities that are not present in the training data, indicating applicability to non-Wikipedia data. Given the large amount of data, our work is also an exercise demonstrating the scalability of our approach.