@article{Chung2017, 
author = {Tonglee Chung and Bin Xu and Yongbin Liu and Juanzi Li and Chunping Ouyang},
title = {How Do Pronouns Affect Word Embedding},
year = {2017},
journal = {Tsinghua Science and Technology},
volume = {22},
number = {6},
pages = {586-594},
keywords = {word embedding, co-reference resolution, representation learning},
url = {https://www.sciopen.com/article/10.23919/TST.2017.8195342},
doi = {10.23919/TST.2017.8195342},
abstract = {Word embedding has drawn a lot of attention due to its usefulness in many NLP tasks. So far a handful of neural-network based word embedding algorithms have been proposed without considering the effects of pronouns in the training corpus. In this paper, we propose using co-reference resolution to improve the word embedding by extracting better context. We evaluate four word embeddings with considerations of co-reference resolution and compare the quality of word embedding on the task of word analogy and word similarity on multiple data sets. Experiments show that by using co-reference resolution, the word embedding performance in the word analogy task can be improved by around 1.88 %. We find that the words that are names of countries are affected the most, which is as expected.}
}