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Linguistic Structure Prediction
Noah A. Smith
出版
Springer Nature
, 2022-05-31
主題
Computers / Artificial Intelligence / General
Computers / Speech & Audio Processing
Language Arts & Disciplines / Linguistics / General
Computers / Information Technology
Computers / Artificial Intelligence / Natural Language Processing
ISBN
3031021436
9783031021435
URL
http://books.google.com.hk/books?id=t4hyEAAAQBAJ&hl=&source=gbs_api
EBook
SAMPLE
註釋
A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference