Language Modelling Makes Sense - WSD (and more) with Contextual Embeddings
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Updated
Jun 12, 2023 - Python
Language Modelling Makes Sense - WSD (and more) with Contextual Embeddings
Incorporating Dictionary Knowledge into Neural Word Sense Disambiguation(ACL 2018)
A Word Sense Disambiguation system integrating implicit and explicit external knowledge.
Disambiguate is a tool for training and using state of the art neural WSD models
A pipeline for transliteration, spell correction, POS tagging and word sense disambiguation of Hinglish code mixed data to Hindi Devanagari script.
Word Sense Induction with BERT MLM
Sequence Labeling in Tensorflow
Data and code for "Nibbling at the Hard Core of Word Sense Disambiguation" (ACL 2022).
Watasense: an Unsupervised WSD System for Under-Resourced Languages.
Repository containing code for the paper "Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation", published at Findings of EMNLP.
NAACL'2021: Non-Parametric Few-Shot Learning for Word Sense Disambiguation
This project is a text summarization system that leverages word sense disambiguation and pre-trained T5 models. It identifies and summarizes repeating words with different meanings, using spaCy for NLP and an n-gram model for candidate word selection. It compares summaries using Rouge scores and outputs the best one. Written in Python with Flask.
Exploiting the global WordNet graph to perform WSD
WSD for Word-in-Context (WiC) disambiguation, experimenting with BERT feature-based and fine-tuning approaches (GlossBERT)
A 78.5% word sense disambiguator based on Transformers and RoBERTa (PyTorch)
Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation
Russian Word Sense Induction system (for the RUSSE'18 shared task)
SIR is a sense-enhanced Information Retrieval system for multiple languages (EMNLP2021).
Repo for the paper "MWE as WSD: Solving Multi-Word Expression Identification with Word Sense Disambiguation"
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