expository
发表于 2025-3-23 12:55:13
Book 2015recent works which utilize different types of information gleaned from unlabeled data. The book offers readers a comprehensive introduction to these approaches, making it ideally suited as a textbook for advanced undergraduate and graduate students and researchers in the fields of syntactic parsing and natural language processing.
JUST
发表于 2025-3-23 17:30:40
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covert
发表于 2025-3-23 21:03:17
Training with Bilexical Dependencies,ng accuracy. First, all the sentences in the unlabeled data are parsed by a baseline parser. Subsequently, information on short dependency relations is extracted from the parsed data, because the accuracies for short dependencies are relatively higher than those for others. Finally, we train another
使人烦燥
发表于 2025-3-24 00:49:33
Training with Meta-features, bi- and tri-gram lexical subtree structures. It can be extended further. The base features defined over surface words, part-of-speech tags represent more complex tree structures than bilexical dependencies and lexical subtrees.
向外供接触
发表于 2025-3-24 02:24:54
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希望
发表于 2025-3-24 07:44:43
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biopsy
发表于 2025-3-24 12:38:21
Dependency Parsing Models,In this chapter, we describe the data-driven supervised dependency parsing models and then summarize the recent reported performance of previous work on Penn English Treebank, a widely used data set.
Lipoprotein
发表于 2025-3-24 16:08:15
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Alveoli
发表于 2025-3-24 22:59:08
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Monotonous
发表于 2025-3-25 01:39:46
Training with Subtree Structures,In this chapter, we introduce a semi-supervised approach of using subtree structures to improve dependency parsing. The subtrees are extracted from dependency trees in auto-parsed data, and a set of subtree-based features is designed for the parsing models.