# De-Conflated Semantic Representations

arXiv: https://arxiv.org/abs/1608.01961 · HTML: https://mlsanity.com/paper/1608.01961/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2016.

## Built on by (most cited first, 12 of 12)

- [Dimensions underlying the representational alignment of deep neural networks with humans](https://mlsanity.com/paper/2406.19087/) (2024)
- [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)
- [Specialized Document Embeddings for Aspect-based Similarity of Research Papers](https://mlsanity.com/paper/2203.14541/) (2022)
- [Large Scale Substitution-based Word Sense Induction](https://mlsanity.com/paper/2110.07681/) (2021)
- [Distributed Representations of Atoms and Materials for Machine Learning](https://mlsanity.com/paper/2107.14664/) (2021)
- [Multi-sense embeddings through a word sense disambiguation process](https://mlsanity.com/paper/2101.08700/) (2021)
- [Enhanced word embeddings using multi-semantic representation through lexical chains](https://mlsanity.com/paper/2101.09023/) (2021)
- [Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change](https://mlsanity.com/paper/2204.05717/) (2022)
- [Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings](https://mlsanity.com/paper/2103.06459/) (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)

## Abstract (arXiv)

One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge it derives from a semantic network. Our approach provides multiple advantages in comparison to the past work, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them.