---
abstract: 'Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize *sequential context* of a word to learn its embedding. While there have been some attempts at utilizing *syntactic context* of a word, such methods result in an explosion of the vocabulary size. In this paper, we overcome this problem by proposing , a flexible Graph Convolution based method for learning word embeddings. utilizes the dependency context of a word without increasing the vocabulary size. Word embeddings learned by outperform existing methods on various intrinsic and extrinsic tasks and provide an advantage when used with ELMo. We also propose , an effective framework for incorporating diverse semantic knowledge for further enhancing learned word representations. We make the source code of both models available to encourage reproducible research.'
author:
- |
    Shikhar Vashishth$^1$ Manik Bhandari$^{2*}$ Prateek Yadav$^3$[^1]\
    **Piyush Rai**$^4$ **Chiranjib Bhattacharyya**$^1$ **Partha Talukdar**$^1$\
    \
    $^1$Indian Institute of Science, $^2$Carnegie Mellon University\
    $^4$Microsoft Research, $^4$IIT Kanpur\
    [{shikhar,chiru,ppt}@iisc.ac.in]{}, [mbhandar@andrew.cmu.edu]{}\
    \
    [t-pryad@microsoft.com, piyush@cse.iitk.ac.in]{}\
bibliography:
- 'acl2019.bib'
title: |
    Incorporating Syntactic and Semantic Information in\
    Word Embeddings using Graph Convolutional Networks
---

Acknowledgments {#acknowledgments .unnumbered}
===============

We thank the anonymous reviewers for their constructive comments. This work is supported in part by the Ministry of Human Resource Development (Government of India) and Google PhD Fellowship.

[^1]:   Contributed equally to the work.
