• Corpus ID: 9128245

Simple English Wikipedia: A New Text Simplification Task

@inproceedings{Coster2011SimpleEW,
  title={Simple English Wikipedia: A New Text Simplification Task},
  author={William Coster and David Kauchak},
  booktitle={Annual Meeting of the Association for Computational Linguistics},
  year={2011}
}
In this paper we examine the task of sentence simplification which aims to reduce the reading complexity of a sentence by incorporating more accessible vocabulary and sentence structure. [] Key Result We provide an analysis of this corpus as well as preliminary results using a phrase-based translation approach for simplification.

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References

SHOWING 1-10 OF 31 REFERENCES

For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia

This work considers two main approaches to deriving simplification probabilities via an edit model that accounts for a mixture of different operations, and using metadata to focus on edits that are more likely to be simplification operations.

Automatic induction of rules for text simplification

Learning Simple Wikipedia: A Cogitation in Ascertaining Abecedarian Language

The potential of Simple Wikipedia to assist automatic text simplification by building a statistical classification system that discriminates simple English from ordinary English is investigated and can be applied as a tool to help writers craft simple text.

Mining Wikipedia Revision Histories for Improving Sentence Compression

This work proposes a novel lexicalized noisy channel model for sentence compression, achieving improved results in grammaticality and compression rate criteria with a slight decrease in importance.

Sentence Alignment for Monolingual Comparable Corpora

This work addresses the problem of sentence alignment for monolingual corpora by incorporating context into the search for an optimal alignment in two complementary ways: learning rules for matching paragraphs using topic structure and refining the matching through local alignment to find good sentence pairs.

Towards Robust Context-Sensitive Sentence Alignment for Monolingual Corpora

A new monolingual sentence alignment algorithm is presented, combining a sentence-based TF*IDF score, turned into a probability distribution using logistic regression, with a global alignment dynamic programming algorithm, achieving a substantial improvement in accuracy over existing systems.

Models for Sentence Compression: A Comparison across Domains, Training Requirements and Evaluation Measures

This paper provides a novel comparison between a supervised constituent-based and an weakly supervised word-based compression algorithm and examines how these models port to different domains (written vs. spoken text).

A Generic Sentence Trimmer with CRFs

The paper presents a novel sentence trimmer in Japanese, which combines a non-statistical yet generic tree generation model and Conditional Random Fields (CRFs), to address improving the

Summarization beyond sentence extraction: A probabilistic approach to sentence compression

Sentence Simplification for Semantic Role Labeling

A general method for learning how to iteratively simplify a sentence, thus decomposing complicated syntax into small, easy-to-process pieces and achieving near-state-of-the-art performance across syntactic variation.