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Titlebook: Cross-Lingual Word Embeddings; Anders S?gaard,Ivan Vuli?,Manaal Faruqui Book 2019 Springer Nature Switzerland AG 2019

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發(fā)表于 2025-3-21 17:23:01 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Cross-Lingual Word Embeddings
編輯Anders S?gaard,Ivan Vuli?,Manaal Faruqui
視頻videohttp://file.papertrans.cn/241/240354/240354.mp4
叢書名稱Synthesis Lectures on Human Language Technologies
圖書封面Titlebook: Cross-Lingual Word Embeddings;  Anders S?gaard,Ivan Vuli?,Manaal Faruqui Book 2019 Springer Nature Switzerland AG 2019
描述.The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano--and most other languages--remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages...In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and res
出版日期Book 2019
版次1
doihttps://doi.org/10.1007/978-3-031-02171-8
isbn_softcover978-3-031-01043-9
isbn_ebook978-3-031-02171-8Series ISSN 1947-4040 Series E-ISSN 1947-4059
issn_series 1947-4040
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 22:57:01 | 只看該作者
板凳
發(fā)表于 2025-3-22 03:17:36 | 只看該作者
WFSA and the Land of the Rising Sun,supervision signal. Focusing on these two dimensions means that, unlike most such typologies in NLP, we initially ignore algorithmic differences between the various approaches and focus on data requirements instead.
地板
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Giovanni Peccati,Murad S. Taqqu across syntactically or semantically similar words; and easy-to-implement, easy-to-train word embedding algorithms (Mikolov et al., 2013a, Pennington et al., 2014) have made high-quality word embeddings accessible for most domains and languages.
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發(fā)表于 2025-3-22 18:00:16 | 只看該作者
WFSA and the Land of the Rising Sun,mply sentence-level alignments exist. Existing approaches to inducing cross-lingual word embeddings from such document collections generally use Wikipedia pages, which come aligned by the Wikipedia concept ID tags.
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Cross-Lingual Word Embedding Models: Typology,supervision signal. Focusing on these two dimensions means that, unlike most such typologies in NLP, we initially ignore algorithmic differences between the various approaches and focus on data requirements instead.
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