This paper describes our submission to ICASSP 2023 MUG Challenge Track 4,
Keyphrase Extraction, which aims to extract keyphrases most relevant to the
conference theme from conference materials. We model the challenge as a
single-class Named Entity Recognition task and developed techniques for better
performance on the challenge: For the data preprocessing, we encode the split
keyphrases after word segmentation. In addition, we increase the amount of
input information that the model can accept at one time by fusing multiple
preprocessed sentences into one segment. We replace the loss function with the
multi-class focal loss to address the sparseness of keyphrases. Besides, we
score each appearance of keyphrases and add an extra output layer to fit the
score to rank keyphrases. Exhaustive evaluations are performed to find the best
combination of the word segmentation tool, the pre-trained embedding model, and
the corresponding hyperparameters. With these proposals, we scored 45.04 on the
final test set.

该研究介绍了他们提交的 ICASSP 2023 MUG 挑战赛第 4 轨道 —— 关键短语提取，并描述了他们开发的技术以改进关键短语提取的性能，包括采用命名实体识别任务的单类建模，用多类焦点损失函数替换丢失函数等。