Accession Number:

ADA624638

Title:

Learning to Understand Natural Language with Less Human Effort

Descriptive Note:

Doctoral thesis

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

2015-05-01

Pagination or Media Count:

189.0

Abstract:

Learning to understand the meaning of natural language is an important problem within language processing that has the potential to revolutionize human interactions with computer systems. Informally, the problem specification is to map natural language text to a formal semantic representation connected to the real world. This problem has applications such as information extraction and understanding robot commands, and also may be helpful for other natural language processing tasks. Human annotation is a significant bottleneck in constructing language understanding systems. These systems have two components that are both constructed using human annotation a semantic parser and a knowledge base. Semantic parsers are typically trained on individually-annotated sentences. Knowledge bases are typically manually constructed and given to the system. While these annotations can be provided in simple settings -- specifically, when the knowledge base is small -- the annotation burden quickly becomes unbearable as the size of the knowledge base increases. More annotated sentences are required to train the semantic parser and the knowledge base itself requires more annotations. Alternative methods to build language understanding systems that require less human annotation are necessary in order to learn to understand natural language in these more challenging settings. This thesis explores alternative supervision assumptions for building language understanding systems with the goal of reducing the annotation burden described above. I focus on two applications information extraction and understanding language in physical environments. In the information extraction application, I present algorithms for training semantic parsers using only predicate instances from a knowledge base and an unlabeled text corpus.

Subject Categories:

  • Linguistics
  • Statistics and Probability
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE