國立虎尾科技大學 |

Discourse-Level Language Understanding with Deep Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Discourse-Level Language Understanding with Deep Learning./
作者:	Iyyer, Mohit.
面頁冊數:	1 online resource (145 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355395303

Discourse-Level Language Understanding with Deep Learning.
Iyyer, Mohit.

Discourse-Level Language Understanding with Deep Learning. - 1 online resource (145 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2017.

Includes bibliographical references

Designing computational models that can understand language at a human level is a foundational goal in the field of natural language processing (NLP). Given a sentence, machines are capable of translating it into many different languages, generating a corresponding syntactic parse tree, marking words that refer to people or places, and much more. These tasks are solved by statistical machine learning algorithms, which leverage patterns in large datasets to build predictive models. Many recent advances in NLP are due to deep learning models (parameterized as neural networks), which bypass user-specified features in favor of building representations of language directly from the text.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355395303Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Discourse-Level Language Understanding with Deep Learning.
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
500 $a Advisers: Jordan Boyd-Graber; Hal Daume.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2017.
504 $a Includes bibliographical references
520 $a Designing computational models that can understand language at a human level is a foundational goal in the field of natural language processing (NLP). Given a sentence, machines are capable of translating it into many different languages, generating a corresponding syntactic parse tree, marking words that refer to people or places, and much more. These tasks are solved by statistical machine learning algorithms, which leverage patterns in large datasets to build predictive models. Many recent advances in NLP are due to deep learning models (parameterized as neural networks), which bypass user-specified features in favor of building representations of language directly from the text.
520 $a Despite many deep learning-fueled advances at the word and sentence level, however, computers still struggle to understand high-level discourse structure in language, or the way in which authors combine and order different units of text (e.g., sentences, paragraphs, chapters) to express a coherent message or narrative. Part of the reason is data-related, as there are no existing datasets for many contextual language-based problems, and some tasks are too complex to be framed as supervised learning problems; for the latter type, we must either resort to unsupervised learning or devise training objectives that simulate the supervised setting. Another reason is architectural: neural networks designed for sentence-level tasks require additional functionality, interpretability, and efficiency to operate at the discourse level. In this thesis, I design deep learning architectures for three NLP tasks that require integrating information across high-level linguistic context: question answering, fictional relationship understanding, and comic book narrative modeling. While these tasks are very different from each other on the surface, I show that similar neural network modules can be used in each case to form contextual representations.
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538 $a Mode of access: World Wide Web
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650 4 $a Artificial intelligence. $3 559380
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