Towards semantic-rich word embeddings

In recent years, word embeddings have been shown to improve the performance in NLP tasks such as syntactic parsing or sentiment analysis. While useful, they are problematic in representing ambiguous words with multiple meanings, since they keep a single representation for each word in the vocabulary...

Full description

Saved in:
Bibliographic Details
Main Authors: Beringer, Grzegorz, Jabtlonski, Mateusz, Januszewski, Piotr, Sobecki, Andrzej, Szymanski, Julian
Format: Conference Proceeding
Language:English
Subjects:
Context modeling
Electronic publishing
Encyclopedias
Internet
Task analysis
Vegetation
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years, word embeddings have been shown to improve the performance in NLP tasks such as syntactic parsing or sentiment analysis. While useful, they are problematic in representing ambiguous words with multiple meanings, since they keep a single representation for each word in the vocabulary. Constructing separate embeddings for meanings of ambiguous words could be useful for solving the Word Sense Disambiguation (WSD) task.In this work, we present how a word embeddings average-based method can be used to produce semantic-rich meaning embeddings. We also open-source a WSD dataset that was created for the purpose of evaluating methods presented in this research.
ISSN:2300-5963
DOI:10.15439/2019F120