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D2KLab at SemEval Task 2: MultiCoNER

This repository contains the code used by the D2KLab team for SemEval Task 2: MultiCoNER. The goal of this task is to detect semantically ambiguous and complex entities in short and low-context settings.

Each folder represents the year we have participated and the experiements we have conducted:

Experiment Results

We evaluated our fine-tuned language model on the test sets of the MultiCoNER datasets. The results are summarized in the tables below:

SemEval 2022 MultiCoNER I

Method BN DE EN ES FA HI KO NL RU TR ZH MIX MULTI Avg.
Official Baseline 39.41 63.74 61.36 57.84 52.24 48.22 55.25 62.01 59.59 46.25 63.4 58.14 48.22 47.78
D2KLab 52.57 67.09 74.57 62.77 55.91 52.78 64.96 66.7 68.21 56.57 54.92 77.6 52.78 68.08

SemEval 2023 MultiCoNER II

Method BN DE EN ES FA FR HI IT PT SV UK ZH Avg.
Official Baseline 1.07 64.61 36.97 49.07 41.28 41.39 2.89 43.13 39.85 69.22 62.08 48.46 41.67
D2KLab 61.43 67.09 61.29 63.17 54.2 64.09 63.29 64.77 60.79 62.98 64.14 54.92 61.84

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