@inproceedings{el-sheikh-etal-2021-integrating,
title = "Integrating Personalized {P}age{R}ank into Neural Word Sense Disambiguation",
author = "El Sheikh, Ahmed and
Bevilacqua, Michele and
Navigli, Roberto",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.715",
doi = "10.18653/v1/2021.emnlp-main.715",
pages = "9092--9098",
abstract = "Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph. However, state-of-the-art approaches have been successful in exploiting only the local structure of the graph, with only close neighbors of a given synset influencing the prediction. In this work, we improve a classification model by recomputing logits as a function of both the vanilla independently produced logits and the global WordNet graph. We achieve this by incorporating an online neural approximated PageRank, which enables us to refine edge weights as well. This method exploits the global graph structure while keeping space requirements linear in the number of edges. We obtain strong improvements, matching the current state of the art. Code is available at \url{https://github.com/SapienzaNLP/neural-pagerank-wsd}",
}
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<abstract>Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph. However, state-of-the-art approaches have been successful in exploiting only the local structure of the graph, with only close neighbors of a given synset influencing the prediction. In this work, we improve a classification model by recomputing logits as a function of both the vanilla independently produced logits and the global WordNet graph. We achieve this by incorporating an online neural approximated PageRank, which enables us to refine edge weights as well. This method exploits the global graph structure while keeping space requirements linear in the number of edges. We obtain strong improvements, matching the current state of the art. Code is available at https://github.com/SapienzaNLP/neural-pagerank-wsd</abstract>
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%0 Conference Proceedings
%T Integrating Personalized PageRank into Neural Word Sense Disambiguation
%A El Sheikh, Ahmed
%A Bevilacqua, Michele
%A Navigli, Roberto
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F el-sheikh-etal-2021-integrating
%X Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph. However, state-of-the-art approaches have been successful in exploiting only the local structure of the graph, with only close neighbors of a given synset influencing the prediction. In this work, we improve a classification model by recomputing logits as a function of both the vanilla independently produced logits and the global WordNet graph. We achieve this by incorporating an online neural approximated PageRank, which enables us to refine edge weights as well. This method exploits the global graph structure while keeping space requirements linear in the number of edges. We obtain strong improvements, matching the current state of the art. Code is available at https://github.com/SapienzaNLP/neural-pagerank-wsd
%R 10.18653/v1/2021.emnlp-main.715
%U https://aclanthology.org/2021.emnlp-main.715
%U https://doi.org/10.18653/v1/2021.emnlp-main.715
%P 9092-9098
Markdown (Informal)
[Integrating Personalized PageRank into Neural Word Sense Disambiguation](https://aclanthology.org/2021.emnlp-main.715) (El Sheikh et al., EMNLP 2021)
ACL