We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings. Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizeable improvements over the state-of-the-art for Bulgarian NER.
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机译：我们提出了一个形态学上的知情模型，用于命名实体识别，它基于LSTM-CRF架构并组合Word Embeddings。 Bi-LSTM字符嵌入，演讲部分（POS）标签和形态学信息。虽然以前的工作已经专注于从原始字输入学习，但仅使用Word and Character Embeddings，我们表明，对于形态学丰富的语言，如保加利亚语，访问POS信息的访问量比详细的形态信息更多地贡献。因此，我们表明命名实体识别只需要粗糙的POS标签，但同时它可以使用不同粒度的一些POS信息来获益。我们的评估结果在标准数据集上显示了对保加利亚人最先进的大量改进。
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