论文标题
梵语的单词分割和形态解析
Word Segmentation and Morphological Parsing for Sanskrit
论文作者
论文摘要
我们描述了我们参与梵语黑客马拉松一词的分割和形态解析（WSMP）。我们通过预测从哪些分割得出的编辑操作来将单词分割任务作为序列标记任务。我们通过预测形态学标签和规则来应对形态分析任务，这些标签和规则将词汇转化为相应的茎。此外，我们提出了一个可端到端的可训练管道模型，用于关节分割和形态分析。我们的模型在联合分割和分析子任务（80.018 F1得分）中表现最佳，并在单个子任务中表现出色（分段：96.189 F1分数 /分析：69.180 F1得分）。 最后，我们分析了模型犯的错误，并建议将来的工作以及有关数据和评估的可能改进。
We describe our participation in the Word Segmentation and Morphological Parsing (WSMP) for Sanskrit hackathon. We approach the word segmentation task as a sequence labelling task by predicting edit operations from which segmentations are derived. We approach the morphological analysis task by predicting morphological tags and rules that transform inflected words into their corresponding stems. Also, we propose an end-to-end trainable pipeline model for joint segmentation and morphological analysis. Our model performed best in the joint segmentation and analysis subtask (80.018 F1 score) and performed second best in the individual subtasks (segmentation: 96.189 F1 score / analysis: 69.180 F1 score). Finally, we analyse errors made by our models and suggest future work and possible improvements regarding data and evaluation.
