# Task-Oriented Learning of Word Embeddings for Semantic Relation Classification

arXiv: https://arxiv.org/abs/1503.00095 · HTML: https://mlsanity.com/paper/1503.00095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2015.

## Built on by (most cited first, 1 of 1)

- [SoftNER: Mining Knowledge Graphs From Cloud Incidents](https://mlsanity.com/paper/2101.05961/) (2021)

## Abstract (arXiv)

We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific features on a large unlabeled corpus. This allows us to explicitly incorporate relation-specific information into the word embeddings. The learned word embeddings are then used to construct feature vectors for a relation classification model. On a well-established semantic relation classification task, our method significantly outperforms a baseline based on a previously introduced word embedding method, and compares favorably to previous state-of-the-art models that use syntactic information or manually constructed external resources.