--- title: "Learning a Lexical Simplifier Using Wikipedia — Horn 2014" description: "The paper addresses lexical simplification: replacing difficult words with easier ones that keep the meaning and still fit the sentence." authors: ["Colby Horn", "Cathryn Manduca", "David Kauchak"] year: 2014 venue: "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)" source: "https://aclanthology.org/P14-2075/" doi: "10.3115/v1/p14-2075" 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/en/papers/horn-2014-learning-lexical-simplifier-wikipedia/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/horn-2014-learning-lexical-simplifier-wikipedia/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) / Learning a Lexical Simplifier Using Wikipedia # Learning a Lexical Simplifier Using Wikipedia **Authors:** Colby Horn, Cathryn Manduca, [David Kauchak](https://md.research.elamur.ai/free/en/authors/david-kauchak/) · **Year:** 2014 · **Venue:** Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · **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/P14-2075/) · [PDF](https://aclanthology.org/P14-2075.pdf) · [DOI 10.3115/v1/p14-2075](https://doi.org/10.3115/v1/p14-2075) · [OpenAlex](https://openalex.org/W2250365808) **Citations:** 112 citations (openalex-snapshot · 2026-10-14) · [86](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper addresses lexical simplification: replacing difficult words with easier ones that keep the meaning and still fit the sentence. Earlier rule-based systems suffered from low coverage and from applying substitutions in the wrong context. The authors take 137K sentence pairs from English Wikipedia aligned with Simple English Wikipedia, word-align them with GIZA++, and filter the aligned word pairs to collect candidate substitutions. They then expand these rules with morphological variants. To choose a substitution in context, they train an SVM rank model with frequency, language-model and n-gram context features. The original word is included as one of the candidates, so the model can also decide to leave the word unchanged. For training and evaluation, they gathered 500 examples, each labelled by 50 Mechanical Turk workers. Their system reaches 76.1% precision and changes 86.3% of the examples, outperforming both a frequency baseline and the same ranker applied to the rules of Biran et al. The work shows that aligned parallel data can produce a much larger rule set without losing quality. ## Key points - **Rule extraction:** - Rules come from the Coster and Kauchak (2011) corpus of 137K normal/simple Wikipedia sentence pairs, word-aligned with GIZA++. - Three filters remove noise: stoplist words, pairs whose POS tags differ (Berkeley parser), and proper nouns. - MorphAdorner adds inflected forms of each rule. - **Rule set size:** The extracted rules cover 14,478 words, with an average of 2.25 candidates per word. The Biran et al. rules cover 3,598 words, with 1.18 candidates per word. - **Ranking features:** The SVM rank model uses: - the probability of a candidate given the word, estimated from the alignments; - unigram frequency in Simple English Wikipedia and in Google n-grams; - four language models built with SRILM using Kneser-Ney smoothing (Simple Wikipedia, English Wikipedia, an even interpolation of those two, and a Google 5-gram model); - Google n-gram counts of the candidate in a context window of one or two words on each side. - **Evaluation data:** 500 examples, each annotated by 50 MTurk workers (25,000 annotations in total). Workers had to be US-based and have at least a 95% acceptance rate. The dataset is publicly released. Results are from 10-fold cross-validation. - **Results (precision / accuracy / changed):** | System | Precision | Accuracy | Changed | |---|---|---|---| | rank-simplify | 76.1% | 66.3% | 86.3% | | frequency baseline | 53.9% | 46.1% | 84.9% | | rank-Biran | 71.4% | 3.4% | 5.2% | Precision@5 for rank-simplify is about 67%. ## Interesting ideas - **"No change" as a candidate:** The original word is added to the candidate list, so deciding whether to simplify at all becomes part of the ranking itself. This removes the need for a separate classifier and could carry over to any substitution task, such as paraphrasing, normalization or terminology replacement. - **Rules from aligned parallel text:** Mining substitution rules from word alignments inside a monolingual sentence-aligned corpus produced a far larger rule set than mining unaligned comparable text. This is worth trying wherever simplified and original versions of documents exist side by side. - **Cheap noise filters plus morphology:** A few simple filters (POS agreement, stoplist, no proper nouns) clean the alignment noise. Symmetric morphological expansion, applied to both sides of a rule, then increases coverage. This is especially relevant for morphologically rich languages, where a rule learned from one word form would otherwise rarely match new text. - **Crowd-ranked evaluation data:** Collecting many free-text suggestions per item and ranking them by how often they occur yields graded gold data. That data supports metrics like precision@k as well as a separate "changed" rate that exposes systems with low coverage. - **Separating rules from ranking:** The ranker works independently of where the rules come from, as shown by applying it to the Biran et al. rules. This allows comparing or combining rule sources from different origins under the same ranking model. *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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) ## Cited by Cited by 86 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 81 not yet. - [Simplifying Lexical Simplification: Do We Need Simplified Corpora?](https://md.research.elamur.ai/free/en/papers/glavas-2015-simplifying-lexical-simplification-do-we/) - [Controllable Text Simplification with Explicit Paraphrasing](https://md.research.elamur.ai/free/en/papers/maddela-2021-controllable-text-simplification-explicit-paraphrasing/) - [SemEval 2016 Task 11: Complex Word Identification](https://md.research.elamur.ai/free/en/papers/paetzold-2016-semeval-2016-task-11-complex/) - [Semantic Structural Evaluation for Text Simplification](https://md.research.elamur.ai/free/en/papers/sulem-2018-semantic-structural-evaluation-text-simplification/) - [Optimizing Statistical Machine Translation for Text Simplification](https://md.research.elamur.ai/free/en/papers/xu-2016-optimizing-statistical-machine-translation-text/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{horn2014learning, title = {{Learning a Lexical Simplifier Using Wikipedia}}, author = {Horn, Colby and Manduca, Cathryn and Kauchak, David}, booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, year = {2014}, doi = {10.3115/v1/p14-2075}, url = {https://doi.org/10.3115/v1/p14-2075} } ``` - [← previous Latent Trees for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/fernandes-2014-latent-trees-coreference-resolution/) - [next → Learning part-of-speech taggers with inter-annotator agreement loss](https://md.research.elamur.ai/free/en/papers/plank-2014-learning-part-speech-taggers-inter/) - [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) ## References 18 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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