We analyze the process of creating word embedding feature representations
designed for a learning task when annotated data is scarce, for example, in
depressive language detection from Tweets. We start with a rich word embedding
pre-trained from a large general dataset, which is then augmented with
embeddings learned from a much smaller and more specific domain dataset through
a simple non-linear mapping mechanism. We also experimented with several other
more sophisticated methods of such mapping including, several auto-encoder
based and custom loss-function based methods that learn embedding
representations through gradually learning to be close to the words of similar
semantics and distant to dissimilar semantics. Our strengthened representations
better capture the semantics of the depression domain, as it combines the
semantics learned from the specific domain coupled with word coverage from the
general language. We also present a comparative performance analyses of our
word embedding representations with a simple bag-of-words model, well known
sentiment and psycholinguistic lexicons, and a general pre-trained word
embedding. When used as feature representations for several different machine
learning methods, including deep learning models in a depressive Tweets
identification task, we show that our augmented word embedding representations
achieve a significantly better F1 score than the others, specially when applied
to a high quality dataset. Also, we present several data ablation tests which
confirm the efficacy of our augmentation techniques.

研究在数据量少的情况下，如何利用数据增强方法，将大规模预训练语料库中的词向量嵌入模型与特定领域语料库中的词向量嵌入模型相结合，以提高在抑郁语言检测任务中的表现，结果显示该数据增强方法可以显著提高模型性能。