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
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