University of Illinois Urbana-Champaign

Semantic modeling of the natural language of Wikipedia annotations

Mu, Jiaqi

Content Files
MU-THESIS-2016.pdf

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https://hdl.handle.net/2142/92851

Description

Title
Semantic modeling of the natural language of Wikipedia annotations
Author(s)
Mu, Jiaqi
Issue Date
2016-07-15
Director of Research (if dissertation) or Advisor (if thesis)
  • Viswanath, Pramod
  • Bhat, Suma P.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2016-11-10T17:55:14Z
Keyword(s)
  • entity embedding
  • knowledge base completion
Abstract
Knowledge bases (KB) store relational facts and constitute a significant resource for a variety of natural language processing (NLP) tasks. Improving their coverage and refining the relations is a basic and pressing research effort. In this thesis we propose a novel approach towards this canonical task by using the unstructured Wikipedia corpus: we extract low-dimensional embeddings for title pages of the Wikipedia corpus and show that they can be used to significantly outperform state-of-the-art approaches on a variety of metrics in three concrete tasks: measuring semantic relatedness, solving semantic analogies, and KB completion and refinement. A central feature of our work is a new log-linear discriminative model for the annotations inside a Wikipedia document that we name IBOE (isotropic bag-of-entities): we hypothesize that the parameters of the model satisfy a geometric symmetry property (isotropy). We show that the isotropy property leads to self-normalization allowing for the design of an efficient parameter estimation algorithm that we christen wiki2vec. The self-normalization property of IBOE is validated empirically on the Wikipedia corpus and is also of independent mathematical interest.
Graduation Semester
2016-08
Type of Resource
text
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http://hdl.handle.net/2142/92851
Copyright and License Information
Copyright 2016 Jiaqi Mu

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