Ballesteros et al., 2017 - Google Patents
Greedy transition-based dependency parsing with stack lstmsBallesteros et al., 2017
View HTML- Document ID
- 16470750030596672745
- Author
- Ballesteros M
- Dyer C
- Goldberg Y
- Smith N
- Publication year
- Publication venue
- Computational Linguistics
External Links
Snippet
We introduce a greedy transition-based parser that learns to represent parser states using recurrent neural networks. Our primary innovation that enables us to do this efficiently is a new control structure for sequential neural networks—the stack long short-term memory unit …
- 230000001537 neural 0 abstract description 62
Classifications
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- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2765—Recognition
- G06F17/277—Lexical analysis, e.g. tokenisation, collocates
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- G—PHYSICS
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- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2705—Parsing
- G06F17/271—Syntactic parsing, e.g. based on context-free grammar [CFG], unification grammars
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- G06F17/2872—Rule based translation
- G06F17/2881—Natural language generation
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- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
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- G06F17/2809—Data driven translation
- G06F17/2827—Example based machine translation; Alignment
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- G06F17/24—Editing, e.g. insert/delete
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- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2755—Morphological analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
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- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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