Motivations and methods for text simplification

Chandrasekar, Raman; Doran, Christine; Bangalore, Srinivas

doi:10.3115/993268.993361

Cited by 226 publications

(163 citation statements)

References 4 publications

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“…Text simplification (TS) is the process of reducing the complexity of a text while preserving its meaning (Chandrasekar et al, 1996;Siddharthan, 2002a;Siddharthan, 2006). There are two main types of simplification: syntactic and lexical.…”

Section: Introductionmentioning

confidence: 99%

Relative clause extraction for syntactic simplification

Dornescu¹,

Evans²,

Orăsan³

2014

Proceedings of the Workshop on Automatic Text Simplification - Methods and Applications in the Multilingual Society (ATS-MA 201

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This paper investigates non-destructive simplification, a type of syntactic text simplification which focuses on extracting embedded clauses from structurally complex sentences and rephrasing them without affecting their original meaning. This process reduces the average sentence length and complexity to make text simpler. Although relevant for human readers with low reading skills or language disabilities, the process has direct applications in NLP. In this paper we analyse the extraction of relative clauses through a tagging approach. A dataset covering three genres was manually annotated and used to develop and compare several approaches for automatically detecting appositions and non-restrictive relative clauses. The best results are obtained by a ML model developed using crfsuite, followed by a rule based method.

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Section: Introductionmentioning

confidence: 99%

Relative clause extraction for syntactic simplification

Dornescu¹,

Evans²,

Orăsan³

2014

Proceedings of the Workshop on Automatic Text Simplification - Methods and Applications in the Multilingual Society (ATS-MA 201

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“…While sentence simplification modifies sentences, replacing lexical items and rearranging order of information, sentence chunking aims to preserve as much of the original sentence as possible. Chandrasekar et al (1996) suggested using dependency structures for simplifying sentences. The authors gave an example of simplifying relative clauses that is similar to chunking but outside of the current scope of our experiments.…”

Section: Related Workmentioning

confidence: 99%

Graph- and surface-level sentence chunking

Muszynska¹

2016

Proceedings of the ACL 2016 Student Research Workshop

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The computing cost of many NLP tasks increases faster than linearly with the length of the representation of a sentence. For parsing the representation is tokens, while for operations on syntax and semantics it will be more complex. In this paper we propose a new task of sentence chunking: splitting sentence representations into coherent substructures. Its aim is to make further processing of long sentences more tractable. We investigate this idea experimentally using the Dependency Minimal Recursion Semantics (DMRS) representation.

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“…in case of understanding disorders [13]), text summarization [10,7], foreign language learning (see [6] for a general presentation). Methods give a significant role to the lexical part, trying to stick to a privileged vocabulary, and to the brevity of sentences.…”

Section: Related Workmentioning

confidence: 99%

From Regulatory Texts to BRMS: How to Guide the Acquisition of Business Rules?

Guisse

Fran

Lévy

et al. 2012

Rules on the Web: Research and Applications

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Abstract. This paper tackles the problem of rule acquisition, which is critical for the development of BRMS. The proposed approach assumes that regulations written in natural language (NL) are an important source of knowledge but that turning them into formal statements is a complex task that cannot be fully automated. The present paper focuses on the first phase of this acquisition process, the normalization phase that aims at transforming NL statements into controlled language (CL), rather than on their formalization into an operational rule base. We show that turning a NL text into a set of self-sufficient and independent CL rules is itself a complex task that involves some lexical and syntactic normalizations but also the restoration of contextual information and of implicit semantic entities to get a set of self-sufficient and unambiguous rule statements. We also present the SemEx tool that supports the proposed acquisition methodology based on the selection of the relevant text fragments and their progressive and interactive transformation into CL rule statements.

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Motivations and methods for text simplification

Abstract: Lottg alld eolni)licated seltteltces prov(: to b(: a. stumbling block for current systems relying on N[, input. These systenls stand to gaill frolil ntethods that syntacti<:aHy simplily su<:h sentences. ']b simplify a sen= tence, we nee Show more

Cited by 226 publications

References 4 publications

Relative clause extraction for syntactic simplification

Relative clause extraction for syntactic simplification

Graph- and surface-level sentence chunking

From Regulatory Texts to BRMS: How to Guide the Acquisition of Business Rules?

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