# Constructing Cross-lingual Consumer Health Vocabulary with Word-Embedding from Comparable User Generated Content

arXiv: https://arxiv.org/abs/2206.11612 · HTML: https://mlsanity.com/paper/2206.11612/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Offline bilingual word vectors, orthogonal transformations and the inverted softmax](https://mlsanity.com/paper/1702.03859/) (2017)
- [Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces](https://mlsanity.com/paper/1908.06625/) (2019)
- [A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity](https://mlsanity.com/paper/1906.01926/) (2019)
- [Towards more patient friendly clinical notes through language models and ontologies](https://mlsanity.com/paper/2112.12672/) (2021)
- [WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking](https://mlsanity.com/paper/2005.01281/) (2020)
- [An Automated Method to Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource](https://mlsanity.com/paper/2105.08812/) (2021)

## Abstract (arXiv)

The online health community (OHC) is the primary channel for laypeople to share health information. To analyze the health consumer-generated content (HCGC) from the OHCs, identifying the colloquial medical expressions used by laypeople is a critical challenge. The open-access and collaborative consumer health vocabulary (OAC CHV) is the controlled vocabulary for addressing such a challenge. Nevertheless, OAC CHV is only available in English, limiting its applicability to other languages. This research proposes a cross-lingual automatic term recognition framework for extending the English CHV into a cross-lingual one. Our framework requires an English HCGC corpus and a non-English (i.e., Chinese in this study) HCGC corpus as inputs. Two monolingual word vector spaces are determined using the skip-gram algorithm so that each space encodes common word associations from laypeople within a language. Based on the isometry assumption, the framework aligns two monolingual spaces into a bilingual word vector space, where we employ cosine similarity as a metric for identifying semantically similar words across languages. The experimental results demonstrate that our framework outperforms the other two large language models in identifying CHV across languages. Our framework only requires raw HCGC corpora and a limited size of medical translations, reducing human efforts in compiling cross-lingual CHV.