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Shallow parsing

About: Shallow parsing is a research topic. Over the lifetime, 397 publications have been published within this topic receiving 10211 citations.


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Journal Article
Sui Zhifang1
TL;DR: In shallow parsing stage, this paper makes use of word formation to get fake head morpheme information of the target verb, which alleviates the problem of data sparseness, and imporves the performance of the parser with the F-score up to 0.93.
Abstract: Semantic role labeling(SRL)is an important way to get semantic information.Many existing systems forSRL make use of full syntactic parses.But due to the low performance of the existing Chinese parser,the performance of labeling based on the full syntactic parses is still not satisfactory.This paper realizes SRL methods based on shallow parsing.In shallow parsing stage,this paper makes use of word formation to get fake head morpheme information,which alleviates the problem of data sparseness,and imporves the performance of the parser with the F-score up to 0.93.In the stage of semantic role labeling,this paper applies word formation to get morpheme information of the target verb,which describes the structure of word in fine granualrity,and provides more information for semantic role labeling.In addition,this paper also proposes a coarse frame feature as an approximation of the sub-categorization information existing full syntactic parsing.F-score of this semantic role labeling system has reached 0.74,a significant improvements over the best reported SRL performance(0.71) in the literature.

2 citations

Journal Article
TL;DR: Este trabajo ha sido parcialmente subvencionado por los proyectos CICYT TIC 2000-0664-C02-01 y TIC2000-1599-C01-01.
Abstract: Este trabajo ha sido parcialmente subvencionado por los proyectos CICYT TIC2000-0664-C02-01 y TIC2000-1599-C01-01.

2 citations

01 Jun 2011
TL;DR: The combined deep and shallow parsing approach with Head-driven Phrase Structured Grammars, the inference process is introduced and it is shown how background knowledge is integrated into the logical inferences to increase the extent, quality, and accuracy of the content extraction.
Abstract: : Written information for military purposes is available in abundance. Documents are written in many languages. The question is how we can automate the content extraction of these documents. One possible approach is based on shallow parsing (information extraction) with application specific combination of analysis results. One example of this, the ZENON research system, does a partial content analysis of some English, Dari, and Tajik texts. Another principal approach for content extraction is based on a combination of deep and shallow parsing with logical inferences on the analysis results. In the project "Multilingual content analysis with semantic inference on military relevant texts" (mIE) we followed the second approach. In this paper, we present the results of the mIE project. First, we briefly contrast the ZENON project to the mIE project. In the main part of the paper, the mIE project is presented. After explaining the combined deep and shallow parsing approach with Head-driven Phrase Structured Grammars, the inference process is introduced. Then we show how background knowledge (WordNet, YAGO) is integrated into the logical inferences to increase the extent, quality, and accuracy of the content extraction. The prototype also is presented. The presentation includes briefing charts.

2 citations

Book ChapterDOI
01 Jan 2005
TL;DR: This chapter shows how a shallow parser can be constructed as a cascade of MBLP-classifiers and introduces software that can be used for the development of memory-based taggers and chunkers.
Abstract: The goal of this chapter is to show that even complex recursive NLP tasks such as parsing (assigning syntactic structure to sentences using a grammar, a lexicon and a search algorithm) can be redefined as a set of cascaded classification problems with separate classifiers for tagging, chunk boundary detection, chunk labeling, relation finding, etc. In such an approach, input vectors represent a focus item and its surrounding context, and output classes represent either a label of the focus (e.g., part of speech tag, constituent label, type of grammatical relation) or a segmentation label (e.g., start or end of a constituent). In this chapter, we show how a shallow parser can be constructed as a cascade of MBLP-classifiers and introduce software that can be used for the development of memory-based taggers and chunkers. Although in principle full parsing could be achieved in this modular , classification-based way (see section 5.5), this approach is more suited for shallow parsing. Partial or shallow parsing, as opposed to full parsing, recovers only a limited amount of syntactic information from natural language sentences. Especially in applications such as information retrieval, question answering, and information extraction, where large volumes of, often ungrammatical, text have to be analyzed in an efficient and robust way, shallow parsing is useful. For these applications a complete syntactic analysis may provide too much or too little information. For example, in text mining applications such as information extraction, summarization, ontology extraction from text and question answering we are more interested in finding concepts (e.g., simple NPs and VPs) and grammatical relations between their heads (e.g., who did what to whom, when, where, why and how) than in elaborate configurational syntactic 85

2 citations

Journal ArticleDOI
01 May 2012
TL;DR: A new approach to natural‐language chunking using an evolutionary model is proposed that uses previously captured training information to guide the evolution of the model and a multiobjective optimization strategy is used to produce unique quality values for objective functions involving the internal and the external quality of chunking.
Abstract: Identifying syntactical information from natural-language texts requires the use of sophisticated parsing techniques mainly based on statistical and machine-learning methods. However, due to complexity and efficiency issues many intensive natural-language processing applications using full syntactic analysis methods may not be effective when processing large amounts of natural-language texts. These tasks can adequately be performed by identifying partial syntactical information through shallow parsing (or chunking) techniques. In this work, a new approach to natural-language chunking using an evolutionary model is proposed. It uses previously captured training information to guide the evolution of the model. In addition, a multiobjective optimization strategy is used to produce unique quality values for objective functions involving the internal and the external quality of chunking. Experiments and the main results obtained using the model and state-of-the-art approaches are discussed. © 2012 Wiley Periodicals, Inc.

2 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20217
202012
20196
20185
201711
201611