Semantic modeling of the natural language of Wikipedia annotations

Mu, Jiaqi

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

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

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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