Repo for the paper "MWE as WSD: Solving Multi-Word Expression Identification with Word Sense Disambiguation"
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Updated
Aug 24, 2024 - Python
Repo for the paper "MWE as WSD: Solving Multi-Word Expression Identification with Word Sense Disambiguation"
A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.
This repository is for the paper UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). Association for Computational Linguistics.
A project to apply Abbott’s heuristics to natural language software engineering scenarios using NLP.
A pipeline for transliteration, spell correction, POS tagging and word sense disambiguation of Hinglish code mixed data to Hindi Devanagari script.
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.
Language Modelling Makes Sense - WSD (and more) with Contextual Embeddings
Supervised end-to-end neural architecture that learns how to disambiguate words inside a text.
Sense Tagged Instances For Finnish
WSD for Word-in-Context (WiC) disambiguation, experimenting with BERT feature-based and fine-tuning approaches (GlossBERT)
Word Sense Disambigution using a supervised machine learning approach
Code and CoarseWSD-20 datasets for "Language Models and Word Sense Disambiguation: An Overview and Analysis"
knowledge-based word sense disambiguation using hierarchical code representation learning.
Code to train and test Word Sense Disambiguation models based on different pretrained transformers.
Exploiting the global WordNet graph to perform WSD
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