Modeling natural language semantics in learned representations
Abstract/Contents
- Abstract
- The last few years have seen many striking successes from artificial neural network models on hard natural language processing tasks. These models replace complex hand-engineered systems for extracting and representing the meanings of sentences with learned functions that construct and use their own internal vector-based representations. Though these learned representations are effective in many domains, they aren't interpretable in familiar terms and their ability to capture the full range of meanings expressible in language is not yet well understood. In this dissertation, I argue that neural network models are capable of learning to represent and reason with the meanings of sentences to a substantial extent. First, I use entailment experiments over artificial languages to show that existing models can learn to reason logically over clean language-like data. I then present a large new corpus of entailments in English and use experiments on that corpus to show that these abilities extend to natural language as well. Finally, I introduce a new model that uses the semantic principle of compositionality to more efficiently and more effectively learn language from large volumes of data.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Bowman, Samuel Ryan |
|---|---|
| Associated with | Stanford University, Department of Linguistics. |
| Primary advisor | Manning, Christopher D |
| Primary advisor | Potts, Christopher, 1977- |
| Thesis advisor | Manning, Christopher D |
| Thesis advisor | Potts, Christopher, 1977- |
| Thesis advisor | Icard, Thomas |
| Thesis advisor | Liang, Percy |
| Advisor | Icard, Thomas |
| Advisor | Liang, Percy |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Samuel Ryan Bowman. |
|---|---|
| Note | Submitted to the Department of Linguistics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Samuel Ryan Bowman
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