# A Sentence Meaning Based Alignment Method for Parallel Text Corpora Preparation

arXiv: https://arxiv.org/abs/1509.09093 · HTML: https://mlsanity.com/paper/1509.09093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [From Natural Language to Simulations: Applying GPT-3 Codex to Automate Simulation Modeling of Logistics Systems](https://mlsanity.com/paper/2202.12107/) (2022)

## Abstract (arXiv)

Text alignment is crucial to the accuracy of Machine Translation (MT) systems, some NLP tools or any other text processing tasks requiring bilingual data. This research proposes a language independent sentence alignment approach based on Polish (not position-sensitive language) to English experiments. This alignment approach was developed on the TED Talks corpus, but can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence recognition. Some of them value synonyms and semantic text structure analysis as a part of additional information. Minimization of data loss was ensured. The solution is compared to other sentence alignment implementations. Also an improvement in MT system score with text processed with described tool is shown.