# AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes

arXiv: https://arxiv.org/abs/1507.01127 · HTML: https://mlsanity.com/paper/1507.01127/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2015.

## Built on by (most cited first, 19 of 19)

- [LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond](https://mlsanity.com/paper/2105.12449/) (2021)
- [Driving Context into Text-to-Text Privatization](https://mlsanity.com/paper/2306.01457/) (2023)
- [Can Word Sense Distribution Detect Semantic Changes of Words?](https://mlsanity.com/paper/2310.10400/) (2023)
- [Large Scale Substitution-based Word Sense Induction](https://mlsanity.com/paper/2110.07681/) (2021)
- [Multi-sense embeddings through a word sense disambiguation process](https://mlsanity.com/paper/2101.08700/) (2021)
- [Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis](https://mlsanity.com/paper/2102.02111/) (2021)
- [Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy](https://mlsanity.com/paper/2110.02204/) (2021)
- [Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations](https://mlsanity.com/paper/2404.12698/) (2024)
- [Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings](https://mlsanity.com/paper/2103.06459/) (2021)
- [Multi-Sense Embeddings for Language Models and Knowledge Distillation](https://mlsanity.com/paper/2504.06036/) (2025)
- [Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation](https://mlsanity.com/paper/2106.02960/) (2021)
- [A Survey On Neural Word Embeddings](https://mlsanity.com/paper/2110.01804/) (2021)
- [EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses](https://mlsanity.com/paper/2103.00232/) (2021)
- [Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings](https://mlsanity.com/paper/2305.19092/) (2023)
- [Sense representations for Portuguese: experiments with sense embeddings and deep neural language models](https://mlsanity.com/paper/2109.00025/) (2021)
- [Representation Learning of Image Schema](https://mlsanity.com/paper/2207.08256/) (2022)
- [Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources](https://mlsanity.com/paper/2402.13302/) (2024)
- [Semantic Cells: Evolutional Process to Acquire Sense Diversity of Items](https://mlsanity.com/paper/2404.14749/) (2024)
- [Decoder-based Sense Knowledge Distillation](https://mlsanity.com/paper/2602.22351/) (2026)

## Abstract (arXiv)

We present textitAutoExtend, a system to learn embeddings for synsets and lexemes. It is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The synset/lexeme embeddings obtained live in the same vector space as the word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet as a lexical resource, but AutoExtend can be easily applied to other resources like Freebase. AutoExtend achieves state-of-the-art performance on word similarity and word sense disambiguation tasks.