Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Semantic character labeling method of natural language sentence

A semantic role labeling and natural language technology, applied in the field of semantic analysis of natural language, can solve the problems of poor performance of the semantic role labeling method, and achieve the effect of improving performance and high performance

Inactive Publication Date: 2009-06-03
SUZHOU UNIV
View PDF0 Cites 33 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is that the purpose of the present invention is to provide an effective semantic role labeling method for sentences, by establishing a joint derivation model, reducing the impact of automatic syntactic analysis results on the semantic role labeling performance, thereby solving the semantic role labeling based on automatic syntactic analysis The problem with poor method performance

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Semantic character labeling method of natural language sentence
  • Semantic character labeling method of natural language sentence
  • Semantic character labeling method of natural language sentence

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0044] Embodiment: The semantic role labeling task is converted into a classification problem, and a maximum entropy classifier is used for training to obtain a semantic role labeling model. For the syntactic analysis task, it is divided into part-of-speech tagging subtasks, basic phrase recognition subtasks and hierarchical syntactic analysis subtasks. The part-of-speech tagging and basic phrase recognition subtasks are completed by mature modules in existing syntactic analysis software; during syntactic analysis, call The semantic role labeling model obtains semantic role information, takes basic phrase recognition results and semantic information as input, and outputs optimal syntactic analysis results and semantic role labeling results.

[0045] Generation of semantic role annotation model:

[0046] Generate training files: from the labeled corpus, extract features according to the features in Table 1, and generate the required training files;

[0047] Model generation: u...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a semantic character labeling method of a natural language sentence, which is characterized in that Chinese syntax analysis and semantic character label are simultaneously realized by adopting a combined learning model. The invention can simultaneously output the syntax analysis result of one sentence and gives the semantic role labeling result of a predicative by using a combined model. Because semantic information is increased in a syntax analysis model in the combined learning model, a model trained is particularly suitable for a semantic role labeling task. Therefore, the semantic role label output by the model has high performance. Meanwhile, the performance between the result output by a single syntax analysis model and the syntax analysis result output by the combined model is not large. Particularly, the syntax analysis performance can also be improved by adding semantic information.

Description

technical field [0001] The invention relates to a method for semantic analysis of natural language, in particular to a method for analyzing and labeling the semantic roles of natural language sentences, belonging to the field of natural language processing in computational linguistics. Background technique [0002] Semantic analysis is a key problem in natural language processing. As one of the current hot research topics, Semantic Role Labeling (SRL) is a type of Shallow Semantic Parsing, and its essence is to perform shallow semantic analysis at the sentence level. The so-called semantic role labeling means that for a given sentence, each predicate in the sentence is marked with the corresponding semantic components in the sentence, and the corresponding semantic marks are made, such as agent, receiver, tool or adjunct. SRL can be used in question answering systems, information extraction, text summarization, text entailment and other fields, and has broad application pro...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F17/27
Inventor 王红玲朱巧明钱培德孔芳李培峰周国栋钱龙华
Owner SUZHOU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com