# False-Friend Detection and Entity Matching via Unsupervised Transliteration

arXiv: https://arxiv.org/abs/1611.06722 · HTML: https://mlsanity.com/paper/1611.06722/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2016.

## Built on by (most cited first, 4 of 4)

- [XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment](https://mlsanity.com/paper/2104.08597/) (2021)
- [Graph-based Clustering for Detecting Semantic Change Across Time and Languages](https://mlsanity.com/paper/2402.01025/) (2024)
- [Offense Detection in Dravidian Languages using Code-Mixing Index based Focal Loss](https://mlsanity.com/paper/2111.06916/) (2021)
- [The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings](https://mlsanity.com/paper/2309.09783/) (2023)

## Abstract (arXiv)

Transliterations play an important role in multilingual entity reference resolution, because proper names increasingly travel between languages in news and social media. Previous work associated with machine translation targets transliteration only single between language pairs, focuses on specific classes of entities (such as cities and celebrities) and relies on manual curation, which limits the expression power of transliteration in multilingual environment. By contrast, we present an unsupervised transliteration model covering 69 major languages that can generate good transliterations for arbitrary strings between any language pair. Our model yields top-(1, 20, 100) averages of (32.85%, 60.44%, 83.20%) in matching gold standard transliteration compared to results from a recently-published system of (26.71%, 50.27%, 72.79%). We also show the quality of our model in detecting true and false friends from Wikipedia high frequency lexicons. Our method indicates a strong signal of pronunciation similarity and boosts the probability of finding true friends in 68 out of 69 languages.