Language-Agnostic Website Embedding and Classification
- URL: http://arxiv.org/abs/2201.03677v1
- Date: Mon, 10 Jan 2022 22:31:48 GMT
- Title: Language-Agnostic Website Embedding and Classification
- Authors: Sylvain Lugeon, Tiziano Piccardi, Robert West
- Abstract summary: We release a dataset with more than 1M websites in 92 languages with relative labels collected from Curlie.
We introduce Homepage2Vec, a machine-learned model for classifying and embedding websites based on their homepage.
We show that Homepage2Vec correctly classifies websites with a macro-averaged F1-score of 0.90, with stable performance across low- as well as high-resource languages.
- Score: 12.86558129722198
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Currently, publicly available models for website classification do not offer
an embedding method and have limited support for languages beyond English. We
release a dataset with more than 1M websites in 92 languages with relative
labels collected from Curlie, the largest multilingual crowdsourced Web
directory. The dataset contains 14 website categories aligned across languages.
Alongside it, we introduce Homepage2Vec, a machine-learned pre-trained model
for classifying and embedding websites based on their homepage in a
language-agnostic way. Homepage2Vec, thanks to its feature set (textual
content, metadata tags, and visual attributes) and recent progress in natural
language representation, is language-independent by design and can generate
embeddings representation. We show that Homepage2Vec correctly classifies
websites with a macro-averaged F1-score of 0.90, with stable performance across
low- as well as high-resource languages. Feature analysis shows that a small
subset of efficiently computable features suffices to achieve high performance
even with limited computational resources. We make publicly available the
curated Curlie dataset aligned across languages, the pre-trained Homepage2Vec
model, and libraries.
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