--- title: "Motivations and Methods for Text — Chandrasekar 1996" description: "The paper argues that long, syntactically dense sentences, which are common in newswire, cause trouble for NLP systems such as parsers, MT, IR and…" authors: ["R. Chandrasekar", "Christine Doran", "B. Srinivas"] year: 1996 venue: "COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics" source: "https://aclanthology.org/C96-2183/" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/papers/chandrasekar-1996-motivations-methods-text-simplification/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/chandrasekar-1996-motivations-methods-text-simplification/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [1996](https://md.research.elamur.ai/free/papers/1996/) / Motivations and Methods for Text Simplification # Motivations and Methods for Text Simplification **Authors:** R. Chandrasekar, Christine Doran, B. Srinivas · **Year:** 1996 · **Venue:** COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/C96-2183/) · [PDF](https://aclanthology.org/C96-2183.pdf) · [OpenAlex](https://openalex.org/W2042262613) **Citations:** 289 citations (openalex-snapshot · 2026-10-14) · [128](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper argues that long, syntactically dense sentences, which are common in newswire, cause trouble for NLP systems such as parsers, MT, IR and summarizers. Splitting them into several short, self-contained sentences could help. The authors treat simplification as two steps. First, build a structural representation of the sentence. Second, apply rewrite rules at "articulation points", such as phrase boundaries, punctuation, conjunctions and relative pronouns. Full parsing is avoided because it is slow and brittle on exactly the complex sentences that need simplifying. They compare two lightweight front ends: - a Finite State Grammar chunker that yields flat noun and verb groups, from the Vaakya translation prototype; - a Dependency-based model (DSM) built on LTAG supertagging, which adds constituent and dependency information. On newswire text, the DSM handled relative clauses and appositives far better than the FSG approach. Its rules were also more general. The work matters as an early statement of rule-based syntactic simplification. It also spells out the discourse problems that simplified output creates: sentence order, referring expressions and tense. ## Key points - Articulation points include phrase starts and ends, punctuation, subordinating and coordinating conjunctions, and relative pronouns. Domain-specific points can be added. - The FSG system (Vaakya) works in three steps: - It POS-tags words, then chunks them into NP and VP groups, marking number, tense, voice and aspect. - It applies manually ordered rules repeatedly until none fire. - On news stories it turned 224 sentences into 369 simplified ones. - The FSG approach fails on several sentence types: long-distance or crossed dependencies, stacked appositives, and unclear attachments. - The DSM uses a trigram supertagger based on LTAG, which runs in linear time. Slot polarity in the supertags is used to derive dependency links. - On newswire, the two models compared as follows (relative clause and appositive simplification only): | Model | Relative clauses recovered | Appositives recovered | Spurious output | |---|---|---|---| | DSM | 25 of 28 | 14 of 14 | 1 relative clause, 2 appositives | | FSG | 17 | 3 | not reported | ## Interesting ideas - **Shallow but structured input instead of full parsing.** Supertagging gives near-parse information (argument slots, clause types) at tagger speed. This is a useful middle ground wherever full parsers are fragile, for example on long Ukrainian sentences. - **Rules over constituent types, not surface sequences.** One DSM rule for relative clauses works in any position (subject, object, embedded). The FSG approach needed separate variants for each case. Abstracting rules this way cuts rule-set size and coverage gaps. - **Rule ordering as a design control.** The FSG rules are ordered by hand so that frequent transformations fire first and harmful interactions are avoided. They are then applied recursively until nothing changes. This is a simple, transparent recipe for any cascade of rewrite rules. - **Choose the output type by downstream use.** If the simplifier only feeds a parser, the split pieces can stay incomplete and be recombined later. Gaps such as the relativized noun need filling only when the output must be standalone text. This saves effort when readable output is not required. - **Evaluate per construction, counting false positives.** Scoring recall separately for each phenomenon (relative clauses, appositives) and counting spurious extractions gives a sharper diagnostic than a single overall score. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Machine translation](https://md.research.elamur.ai/free/shelves/machine-translation/) · [Natural language processing](https://md.research.elamur.ai/free/shelves/nlp/) ## Cited by Cited by 128 papers of our library cite it (of the 140673 whose bibliographies we read); 20 of them have a page here, 108 not yet. - [Supertagging: An Approach to Almost Parsing](https://md.research.elamur.ai/free/papers/bangalore-1999-supertagging-approach-almost-parsing/) - [Putting it Simply: a Context-Aware Approach to Lexical Simplification](https://md.research.elamur.ai/free/papers/biran-2011-putting-it-simply-context-aware/) - [Neural CRF Model for Sentence Alignment in Text Simplification](https://md.research.elamur.ai/free/papers/jiang-2020-neural-crf-model-sentence-alignment/) - [Controllable Text Simplification with Explicit Paraphrasing](https://md.research.elamur.ai/free/papers/maddela-2021-controllable-text-simplification-explicit-paraphrasing/) - [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://md.research.elamur.ai/free/papers/manchego-2020-asset-dataset-tuning-evaluation-sentence/) - [Data-Driven Sentence Simplification: Survey and Benchmark](https://md.research.elamur.ai/free/papers/manchego-2020-data-driven-sentence-simplification-survey/) - [Document-Level Text Simplification in Estonian Using Large Language Models](https://md.research.elamur.ai/free/papers/muru-2026-document-level-text-simplification-estonian/) - [Unsupervised Sentence Simplification Using Deep Semantics](https://md.research.elamur.ai/free/papers/narayan-2016-unsupervised-sentence-simplification-deep-semantics/) - [Text Simplification using Typed Dependencies: A Comparision of the Robustness of Different Generation Strategies](https://md.research.elamur.ai/free/papers/siddharthan-2011-text-simplification-typed-dependencies-comparision/) - [Hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules](https://md.research.elamur.ai/free/papers/siddharthan-2014-hybrid-text-simplification-synchronous-dependency/) - [BLEU is Not Suitable for the Evaluation of Text Simplification](https://md.research.elamur.ai/free/papers/sulem-2018-bleu-is-not-suitable-evaluation/) - [Semantic Structural Evaluation for Text Simplification](https://md.research.elamur.ai/free/papers/sulem-2018-semantic-structural-evaluation-text-simplification/) - [Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation](https://md.research.elamur.ai/free/papers/wein-2024-lost-translationese-reducing-translation-effect/) - [Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming](https://md.research.elamur.ai/free/papers/woodsend-2011-learning-simplify-sentences-quasi-synchronous/) - [Sentence Simplification by Monolingual Machine Translation](https://md.research.elamur.ai/free/papers/wubben-2012-sentence-simplification-monolingual-machine-translation/) - [Paraphrasing for Style](https://md.research.elamur.ai/free/papers/xu-2012-paraphrasing-style/) - [Problems in Current Text Simplification Research: New Data Can Help](https://md.research.elamur.ai/free/papers/xu-2015-problems-current-text-simplification-research/) - [Optimizing Statistical Machine Translation for Text Simplification](https://md.research.elamur.ai/free/papers/xu-2016-optimizing-statistical-machine-translation-text/) - [Sentence Simplification with Deep Reinforcement Learning](https://md.research.elamur.ai/free/papers/zhang-2017-sentence-simplification-deep-reinforcement-learning/) - [A Monolingual Tree-based Translation Model for Sentence Simplification](https://md.research.elamur.ai/free/papers/zhu-2010-monolingual-tree-based-translation-model/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{chandrasekar1996motivations, title = {{Motivations and Methods for Text Simplification}}, author = {Chandrasekar, R. and Doran, Christine and Srinivas, B.}, booktitle = {COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics}, year = {1996}, doi = {10.3115/993268.993361}, url = {https://doi.org/10.3115/993268.993361} } ``` - [← previous Interrogatives in Biblical Hebrew: a case study in the Joseph narrative](https://md.research.elamur.ai/free/papers/hbo-1996-meister-interrogatives-biblical-hebrew-case-study/) - [next → The Role of the Gricean Maxims in the Generation of Referring Expressions](https://md.research.elamur.ai/free/papers/dale-1996-role-gricean-maxims-generation-referring/) - [↑ back to papers of 1996](https://md.research.elamur.ai/free/papers/1996/) ## References 6 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Steven Abney. 1994. I)epcndency Grammars and Context-Free Grammars. Manuscript, University of Tubingen, March. R. Chandrasekar and S. Ramani. 1 9 9 6 . Automatic Simplifica.tion of Natural Language Text. M~muscript, National Centre for So[tware Technol: ogy, Bombay. [find](https://scholar.google.com/scholar?q=Steven+Abney%2E+1994%2E+I%29epcndency+Grammars+and+Context%2DFree+Grammars%2E+Manuscript%2C+University+of+Tubingen%2C+March%2E+R%2E+Chandrasekar+and+S%2E+Ramani%2E+1+9+9+6+%2E+Automatic+Simplifica%2Etion+of+Natural+Language+Te) 2. R. Chandrasekar. 1994. A Hybrid Approach to Machine Translation using Man Machine Communication. Ph.D. thesis, 'Pata Institute of I"undamcnta] Research/University of Bombay, Bombay. [find](https://scholar.google.com/scholar?q=R%2E+Chandrasekar%2E+1994%2E+A+Hybrid+Approach+to+Machine+Translation+using+Man+Machine+Communication%2E+Ph%2ED%2E+thesis%2C+%27Pata+Institute+of+I%22undamcnta%5D+Research%2FUniversity+of+Bombay%2C+Bombay%2E) 3. Ralph Grishman. 1995. Where's the Syntax? The New York University M UC-6 System. [n P~occe(g ings of the Sixth Message Understanding Conference, Columbia, Maryland. Jerry Hobbs, Doug Appelt, John Bear, ])avid Israel, and W. Mary Tyson. 11992. FAST(IS: a system, for extracting information from natural language text. Technical Report 519, SRI. [find](https://scholar.google.com/scholar?q=Ralph+Grishman%2E+1995%2E+Where%27s+the+Syntax%3F+The+New+York+University+M+UC%2D6+System%2E+%5Bn+P%7Eocce%28g+ings+of+the+Sixth+Message+Understanding+Conference%2C+Columbia%2C+Maryland%2E+Jerry+Hobbs%2C+Doug+Appelt%2C+John+Bear) 4. Aravind K. Joshi and B. Srinivas. 1994. Disambiguation of Super Parts of Speech (or Supertags): Almost Parsing. In Proceedings of the 17 th International Conference on Computational Linguistics (COLING '94), Kyoto, Japan, August. 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In Proceedings of the 31'~t Conference of Association of Computational Linguistics, Ohio State University, Columbus, Ohio. [find](https://scholar.google.com/scholar?q=Richard+II%2E+Wojcik%2C+Philip+tIarrison%2C+%2Crod+John+Bremer%2E+1993%2E+Using+bracketed+parses+to+evMuate+a+grain%2Dmar+checking+%3Brpplication%2E+In+Proceedings+of+the+31%27%7Et+Conference+of+Association+of+Computationa)