Researches on Indonesian named entity (NE) tagger have been conducted since
years ago. However, most did not use deep learning and instead employed
traditional machine learning algorithms such as association rule, support
vector machine, random forest, na\"ive bayes, etc. In those researches, word
lists as gazetteers or clue words were provided to enhance the accuracy. Here,
we attempt to employ deep learning in our Indonesian NE tagger. We use long
short-term memory (LSTM) as the topology since it is the state-of-the-art of NE
tagger. By using LSTM, we do not need a word list in order to enhance the
accuracy. Basically, there are two main things that we investigate. The first
is the output layer of the network: Softmax vs conditional random field (CRF).
The second is the usage of part of speech (POS) tag embedding input layer.
Using 8400 sentences as the training data and 97 sentences as the evaluation
data, we find that using POS tag embedding as additional input improves the
performance of our Indonesian NE tagger. As for the comparison between Softmax
and CRF, we find that both architectures have a weakness in classifying an NE
tag.

本文使用深度学习中的长短时记忆模型和条件随机场模型，通过使用 8400 句训练语料和 97 句测试语料，研究了词性嵌入和模型结构对印尼命名实体识别的影响。结果表明，使用词性嵌入作为额外输入有助于提高识别性能，但 Softmax 和 CRF 两种模型结构都存在命名实体分类的弱点。