國立虎尾科技大學 |

Learning Representations of Text through Language and Discourse Modeling : = From Characters to Sentences.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Learning Representations of Text through Language and Discourse Modeling :/
其他題名:	From Characters to Sentences.
作者:	Jernite, Yacine.
面頁冊數:	1 online resource (213 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355773569

Learning Representations of Text through Language and Discourse Modeling : = From Characters to Sentences.
Jernite, Yacine.

Learning Representations of Text through Language and Discourse Modeling :From Characters to Sentences. - 1 online resource (213 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--New York University, 2018.

Includes bibliographical references

In this thesis, we consider the problem of obtaining a representation of the meaning expressed in a text. How to do so correctly remains a largely open problem, combining a number of inter-related questions (e.g. what is the role of context in interpreting text? how should language understanding models handle compositionality? etc...) In this work, after reflecting on the notion of meaning and describing the most common sequence modeling paradigms in use in recent work, we focus on two of these questions: what level of granularity text should be read at, and what training objectives can lead models to learn useful representations of a text's meaning.

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

Mode of access: World Wide Web

ISBN: 9780355773569Subjects--Topical Terms:

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

554714
Electronic books.

Learning Representations of Text through Language and Discourse Modeling : = From Characters to Sentences.
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: David Sontag.
502 $a Thesis (Ph.D.)--New York University, 2018.
504 $a Includes bibliographical references
520 $a In this thesis, we consider the problem of obtaining a representation of the meaning expressed in a text. How to do so correctly remains a largely open problem, combining a number of inter-related questions (e.g. what is the role of context in interpreting text? how should language understanding models handle compositionality? etc...) In this work, after reflecting on the notion of meaning and describing the most common sequence modeling paradigms in use in recent work, we focus on two of these questions: what level of granularity text should be read at, and what training objectives can lead models to learn useful representations of a text's meaning.
520 $a In a first part, we argue for the use of sub-word information for that purpose, and present new neural network architectures which can either process words in a way that takes advantage of morphological information, or do away with word separations altogether while still being able to identify relevant units of meaning.
520 $a The second part starts by arguing for the use of language modeling as a learning objective, and provides algorithms which can help with its scalability issues and propose a globally rather than locally normalized probability distribution. It then explores the question of what makes a good language learning objective, and introduces discriminative objectives inspired by the notion of discourse coherence which help learn a representation of the meaning of sentences.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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