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You searched for +publisher:"University of Texas – Austin" +contributor:("Smith, Noah A"). One record found.

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University of Texas – Austin

1. Garrette, Daniel Hunter. Inducing grammars from linguistic universals and realistic amounts of supervision.

Degree: Computer Sciences, 2015, University of Texas – Austin

The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance can be achieved with plentiful supervised data. However, such resources are extremely costly to produce, making them an unlikely option for building NLP tools in under-resourced languages or domains. This dissertation is concerned with reducing the annotation required to learn NLP models, with the goal of opening up the range of domains and languages to which NLP technologies may be applied. In this work, we explore the possibility of learning from a degree of supervision that is at or close to the amount that could reasonably be collected from annotators for a particular domain or language that currently has none. We show that just a small amount of annotation input — even that which can be collected in just a few hours — can provide enormous advantages if we have learning algorithms that can appropriately exploit it. This work presents new algorithms, models, and approaches designed to learn grammatical information from weak supervision. In particular, we look at ways of intersecting a variety of different forms of supervision in complementary ways, thus lowering the overall annotation burden. Sources of information include tag dictionaries, morphological analyzers, constituent bracketings, and partial tree annotations, as well as unannotated corpora. For example, we present algorithms that are able to combine faster-to-obtain type-level annotation with unannotated text to remove the need for slower-to-obtain token-level annotation. Much of this dissertation describes work on Combinatory Categorial Grammar (CCG), a grammatical formalism notable for its use of structured, logic-backed categories that describe how each word and constituent fits into the overall syntax of the sentence. This work shows how linguistic universals intrinsic to the CCG formalism itself can be encoded as Bayesian priors to improve learning. Advisors/Committee Members: Baldridge, Jason (advisor), Mooney, Raymond J. (Raymond Joseph) (advisor), Ravikumar, Pradeep (committee member), Scott, James G (committee member), Smith, Noah A (committee member).

Subjects/Keywords: Computer science; Artificial intelligence; Natural language processing; Machine learning; Bayesian statistics; Grammar induction; Parsing; Computational linguistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Garrette, D. H. (2015). Inducing grammars from linguistic universals and realistic amounts of supervision. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44478

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Thesis, University of Texas – Austin. Accessed June 25, 2019. http://hdl.handle.net/2152/44478.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Web. 25 Jun 2019.

Vancouver:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2152/44478.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/44478

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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