자료유형  

 학위논문

Control Number  

0015490782

International Standard Book Number  

9781085565288

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Upadhyay, Shyam.

Publication, Distribution, etc. (Imprint  

[Sl] : University of Pennsylvania, 2019

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2019

Physical Description  

210 p

General Note  

Source: Dissertations Abstracts International, Volume: 81-02, Section: B.

General Note  

Advisor: Roth, Dan.

Dissertation Note  

Thesis (Ph.D.)--University of Pennsylvania, 2019.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Traditional approaches to supervised learning require a generous amount of labeled data for good generalization. While such annotation-heavy approaches have proven useful for some Natural Language Processing (NLP) tasks in high-resource languages (like English), they are unlikely to scale to languages where collecting labeled data is di cult and time-consuming. Translating supervision available in English is also not a viable solution, because developing a good machine translation system requires expensive to annotate resources which are not available for most languages.In this thesis, I argue that cross-lingual representations are an effective means of extending NLP tools to languages beyond English without resorting to generous amounts of annotated data or expensive machine translation. These representations can be learned in an inexpensive manner, often from signals completely unrelated to the task of interest. I begin with a review of different ways of inducing such representations using a variety of cross-lingual signals and study algorithmic approaches of using them in a diverse set of downstream tasks. Examples of such tasks covered in this thesis include learning representations to transfer a trained model across languages for document classification, assist in monolingual lexical semantics like word sense induction, identify asymmetric lexical relationships like hypernymy between words in different languages, or combining supervision across languages through a shared feature space for cross-lingual entity linking. In all these applications, the representations make information expressed in other languages available in English, while requiring minimal additional supervision in the language of interest.

Subject Added Entry-Topical Term  

Computer science

Added Entry-Corporate Name  

University of Pennsylvania Computer and Information Science

Host Item Entry  

Dissertations Abstracts International. 81-02B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:566985