# Incorporating Both Distributional and Relational Semantics in Word Representations

arXiv: https://arxiv.org/abs/1412.5836 · HTML: https://mlsanity.com/paper/1412.5836/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2014.

## Built on by (most cited first, 3 of 3)

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- [Lexical semantics enhanced neural word embeddings](https://mlsanity.com/paper/2210.00754/) (2022)

## Abstract (arXiv)

We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics. To this end, we employ the Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a distributional objective on raw text and a relational objective on WordNet. Preliminary results on knowledge base completion, analogy tests, and parsing show that word representations trained on both objectives can give improvements in some cases.