Fine-grained entity typing system - design and analysis
Muddireddy, Pavankumar Reddy
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https://hdl.handle.net/2142/101214
Description
Title
Fine-grained entity typing system - design and analysis
Author(s)
Muddireddy, Pavankumar Reddy
Issue Date
2018-04-24
Director of Research (if dissertation) or Advisor (if thesis)
Roth, Dan
Department of Study
Electrical & Computer Eng
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
named entity recognition
NER
fine-grained named entity recognition
finet
figer
information retrieval
entity typing
fine-grained typing
Abstract
Named entity recognition (NER) is a natural language processing (NLP) task that involves identifying mentions (spans of text) denoting entities in a given text document and assigning them a semantic category/type from a given taxonomy. It is considered to be one of the fundamental tasks in NLP and forms the basis for higher level understanding. In this thesis, we deal with fine-grained entity type recognition, which is a variant of the classic NER task where the usual types are sub-divided into fine-grained types. We show that the current approaches, which address this problem using only local context, are insufficient to completely address the problem. We systematically identify the fundamental challenges and misconceptions that underlie the assumptions, approaches and evaluation methodologies of this task and propose improvements and alternatives. We do this by first analyzing the role of context and background knowledge in the task of fine-grained entity typing. Second, we introduce a modular architecture for fine-grained typing of entities and show that a rather simple instantiation of these modules reaches the state-of-the-art performance.
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