基于DAN与FastText的藏文短文本分类研究

Study on Tibetan Short Text Classification Based on DAN and FastText

随着藏文信息不断融入社会生活,越来越多的藏文短文本数据存在网络平台上。针对传统分类方法在藏文短文本上分类性能低的问题,文中提出了一种基于DAN-FastText的藏文短文本分类模型。该模型使用FastText网络在较大规模的藏文语料上进行无监督训练获得预训练的藏文音节向量集,使用预训练的音节向量集将藏文短文本信息转化为音节向量,把音节向量送入DAN(Deep Averaging Networks)网络并在输出阶段融合经过FastText网络训练的句向量特征,最后通过全连接层和softmax层完成分类。在公开的TNCC(Tibetan News Classification Corpus)新闻标题数据集上所提模型的Macro-F1是64.53%,比目前最好评测结果TiBERT模型的Macro-F1得分高出2.81%,比GCN模型的Macro-F1得分高出6.14%,融合模型具有较好的藏文短文本分类效果。

As Tibetan information continues to be integrated into social life,more and more Tibetan short text data is available on online platforms.Aiming at the low classification performance of traditional classification methods on Tibetan short texts,a Tibetan short text classification model based on DAN-FastText is proposed.The model uses the FastText network to perform unsupervised training on a large-scale Tibetan corpus to obtain the pre-trained Tibetan syllabic vector set,uses the pre-trained syllable vector set to convert the Tibetan short text information into syllable vector,sends the syllable vector into the deep averaging networks(DAN)network and fuses the sentence vector features trained by the FastText network in the output stage,and finally completes the classification through the fully connected layer and the softmax layer.On the publicly available tibetan news classification corpus(TNCC)news headline dataset,Macro-F1 is 64.53%,which is 2.81%higher than that of the TiBERT model and 6.14%higher than that GCN model,and the fusion model has a better Tibetan short text classification effect.