Relation Extraction With Synthetic Explanations And Neural Network

• Author / Creator

  Rozan Chahardoli

• Relation Extraction, which is defined as the detection of existing relations between a
  pair of entities in a sentence, has received a large interest lately, including more recent
  work on using neural methods. Since neural systems need a large number of annotated
  sentences to build effective models, Distant Supervision has been a preferred choice
  for collecting training labeled data. However, recent published work has shown that,
  training classifiers via a small number of annotated data and some explanation of why
  a sentence expresses a relation performs as accurate as distant supervision methods
  working with a large number of annotated sentences. In this thesis, we show that we
  can generate synthetic explanations, based on a small number of trigger words, for
  each relation in a way that the resulting explanations achieve comparable accuracy to
  human produced explanations by training a neural classifier. Our system is evaluated on
  five relation extraction tasks with different entity types (person-person, person-location,
  etc.) and the results show that synthetic explanations can work as precise as human
  generated explanations for the task of relation extraction. The proposed system also
  has the ability to classify noisy data coming from distant supervision methods with a
  reasonable accuracy.

• Subjects / Keywords

  • Sentence Embedding
  • Neural Network
  • Distant Supervision
  • Relation Extraction
  • Named Entity Recognition

• Graduation date

  Fall 2019

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-84jn-cr09

• License

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