FUSE: Multi-faceted set expansion by coherent clustering of skip-grams

"Set expansion aims to expand a small set of seed entities into a complete set of relevant entities. Most existing approaches assume the input seed set is unambiguous and completely ignore the multi-faceted semantics of seed entities. As a result, given the seed set {""Canon"", ""Sony"", ""Nikon""}, previous methods return one mixed set of entities that are either camera brands or Japanese companies. In this thesis, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and returns multiple sets of entities, one for each semantic facet. We propose an unsupervised framework, FUSE, which consists of three major components: (1) facet discovery module: identifies all semantic facets of each seed entity by extracting and clustering its skip-grams, (2) facet fusion module: discovers shared semantic facets of the entire seed set by an optimization formulation, and (3) entity expansion module: expands each semantic facet by utilizing an iterative algorithm robust to skip-gram noise. Extensive experiments demonstrate that our algorithm, FUSE, can accurately identify multiple semantic facets of the seed set and generate quality entities for each facet."

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Zhu, Wanzheng

Contributors dc:contributor

• Han, Jiawei

• Set Expansion, Word Sense Disambiguation

Statement dc:rights

• Copyright 2019 Wanzheng Zhu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/105012

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105012