---
title: "A System for Automatic English Text Expansion — Mendez 2019"
description: "The authors build on their earlier Spanish system. 75 references. Among them: 8."
authors: ["Silvia Garcia Mendez", "Milagros Fernandez Gavilanes", "Enrique Costa Montenegro", "Jonathan Juncal Martinez", "Francisco Javier Gonzalez Castano", "Ehud Reiter"]
year: 2019
venue: "arXiv:2405.18350 · (2019) IEEE Access, 7, 123320-123333"
source: "https://arxiv.org/abs/2405.18350"
doi: "10.1109/access.2019.2937505"
arxiv: "2405.18350"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mendez-2024-system-automatic-english-text-expansion/"
---
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# A System for Automatic English Text Expansion

**Authors:** Silvia Garcia Mendez, Milagros Fernandez Gavilanes, Enrique Costa Montenegro, Jonathan Juncal Martinez, Francisco Javier Gonzalez Castano and [Ehud Reiter](https://md.research.elamur.ai/free/en/authors/ehud-reiter/) · **Year:** 2019 · **Venue:** [arXiv:2405.18350 · (2019) IEEE Access, 7, 123320-123333](https://md.research.elamur.ai/free/en/venues/ieee-access/) ([IEEE Access 2019](https://md.research.elamur.ai/free/en/venues/ieee-access/2019/)) · **Publisher:** [Institute of Electrical and Electronics Engineers](https://md.research.elamur.ai/free/en/publishers/institute-of-electrical-and-electronics-engineers/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1109/access.2019.2937505) · [PDF](https://arxiv.org/pdf/2405.18350) · [DOI 10.1109/access.2019.2937505](https://doi.org/10.1109/access.2019.2937505) · [arXiv 2405.18350](https://arxiv.org/abs/2405.18350) · [OpenAlex](https://openalex.org/W2971252977)

**Citations:** 8 citations (OpenAlex, 2026-10-14)

## Summary
The paper addresses text expansion: turning a handful of content words (for example "she, look, picture, yesterday, not") into a complete, grammatical English sentence. Its main motivation is Augmentative and Alternative Communication (AAC), where a user picks pictograms and the device must produce fluent text. The authors build on their earlier Spanish system. They describe a hybrid NLG pipeline in which hand-built linguistic knowledge (a lexicon plus a grammar) drives a sentence planner and a surface realiser, and a statistical language model guesses missing prepositions. A central resource is aLexiE, a wide-coverage English lexicon assembled without human supervision. It merges EnLex, the NIH Specialist Lexicon and the Freeling English dictionary, then checks the result against Merriam-Webster. The system regenerated about 72% of sentences from an AAC corpus correctly. On a new 948-sentence Spanish–English parallel corpus, English reached 72.26% versus 77.64% for Spanish, with most English errors involving prepositions and phrasal verbs. The modular split between language-dependent and language-independent parts makes porting to other languages cheap.

## Key points
- aLexiE is built from EnLex (Alexina Project), the NIH Specialist Lexicon and EN-FREELING. Entries are converted to the extensional Alexina format, merged through graph unification, and checked against the Merriam-Webster Dictionary.
- The architecture has a lexicon and a grammar as the only language-dependent parts, plus a domain-independent sentence planner and surface realiser. A language model trained on Wikipedia infers prepositions.
- On regeneration of AAC corpus sentences, 72.23% were produced correctly.
- Five annotators labelled errors using six error types. Overall agreement was Alpha 0.582 and accuracy 0.691, and pairwise values are also reported.
- On the parallel corpus of 948 sentences, success was 72.26% for English and 77.64% for Spanish. The dominant English failures were missing or wrong prepositions and verb tense adjustment, which the authors link to phrasal verbs.

## Interesting ideas
- Building a lexicon by automatically merging several free resources into one shared format, then cross-checking entries and categories against an independent dictionary, gives broad coverage without manual curation.
- Statistics are kept for one narrow, hard decision (which preposition to insert), and everything else stays rule-based. This keeps the system transparent while patching its weakest spot.
- Evaluation by regeneration: strip a corpus sentence down to its content words, expand it again, and compare the output with the original. This yields automatic gold data with no new annotation.
- The error analysis uses a fixed set of error categories and a coincidence matrix over several annotators. This shows where judges disagree, not just how often.
- Comparing the same system on a parallel corpus in two languages separated language-specific difficulty (preposition predictability, phrasal verbs) from system weaknesses.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): The same extract → unify → verify loop could merge lemma/POS data from several sources for the `en` crate, and log every disagreement as an explainable conflict.
- **I2** (back): A generator from the meaning graph could stay rule-driven but call a small scored model only for preposition and particle choice, recording the alternatives it considered.
- **I3** (back): The text → graph → text compressor can be tested the same way, by checking whether rebuilding a sentence from its frames recovers the original wording.
- **I4** (learn): Disagreements among witness parsers could be binned into typed error categories with a coincidence matrix, to see which error types are inherently unstable before they are trusted as silver data.
- **I5** (world): Phrasal verbs could be listed as single entries in the compiled verb-class knowledge, so that frames and generation do not have to rebuild the particle from context.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Aberdeen](https://md.research.elamur.ai/free/en/universities/university-of-aberdeen/) — University, Aberdeen, United Kingdom · 20 works · 1,385 citations
- [Universidade de Vigo](https://md.research.elamur.ai/free/en/universities/universidade-de-vigo/) — University, Vigo, Spain · 5 works · 12 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works

## Cite

Cite as: Silvia Garcia Mendez, Milagros Fernandez Gavilanes, Enrique Costa Montenegro, Jonathan Juncal Martinez, Francisco Javier Gonzalez Castano and Ehud Reiter. 2019. A System for Automatic English Text Expansion. arXiv:2405.18350 · (2019) IEEE Access, 7, 123320-123333.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{mendez2019system,
  title = {{A System for Automatic English Text Expansion}},
  author = {Silvia Garcia Mendez, Milagros Fernandez Gavilanes, Enrique Costa Montenegro, Jonathan Juncal Martinez, Francisco Javier Gonzalez Castano and Ehud Reiter},
  year = {2019},
  doi = {10.1109/access.2019.2937505},
  eprint = {2405.18350},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1109/access.2019.2937505}
}
```

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*Updated 1 November 2026*