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Found 6 papers, 2 papers with code
Persian SemCor: A Bag of Word Sense Annotated Corpus for the Persian Language
Supervised approaches usually achieve the best performance in the Word Sense Disambiguation problem.
DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models
The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages. After training a variety of contextual language models on the NER dataset, we used an ensemble strategy based on a majority vote to finalize our model.
Multilingual Named Entity Recognition
named-entity-recognition
+2
DS4DH at #SMM4H 2023: Zero-Shot Adverse Drug Events Normalization using Sentence Transformers and Reciprocal-Rank Fusion
This paper outlines the performance evaluation of a system for adverse drug event normalization, developed by the Data Science for Digital Health (DS4DH) group for the Social Media Mining for Health Applications (SMM4H) 2023 shared task 5.
Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events
Current approaches for clinical information extraction are inefficient in terms of computational costs and memory consumption, hindering their application to process large-scale electronic health records (EHRs).
Persian-WSD-Corpus: A Sense Annotated Corpus for Persian All-words Word Sense Disambiguation
In this paper, we address this shortage for the Persian language by introducing SBU-WSD-Corpus, as the first standard test set for the Persian All-words WSD task.
Knowledge-Based Word Sense Disambiguation with Distributional Semantic Expansion
In this paper, we presented a WSD system that uses LDA topics for semantic expansion of document words.
