Unsupervised word embeddings have become a popular approach of word
representation in NLP tasks. However there are limitations to the semantics
represented by unsupervised embeddings, and inadequate fine-tuning of
embeddings can lead to suboptimal performance. We propose a novel learning
technique called Delta Embedding Learning, which can be applied to general NLP
tasks to improve performance by optimized tuning of the word embeddings. A
structured regularization is applied to the embeddings to ensure they are tuned
in an incremental way. As a result, the tuned word embeddings become better
word representations by absorbing semantic information from supervision without
"forgetting." We apply the method to various NLP tasks and see a consistent
improvement in performance. Evaluation also confirms the tuned word embeddings
have better semantic properties.

提出了一种新颖的学习技术 ——Delta Embedding Learning 以优化调整单词嵌入，并通过结构化正则化确保了单词嵌入的逐步调整，提高了各种 NLP 任务的性能，并证实调整后的单词嵌入具有更好的语义属性。

Delta Embedding Learning

Recently, due to the increasing popularity of social media, the necessity for
extracting information from informal text types, such as microblog texts, has
gained significant attention. In this study, we focused on the Named Entity
Recognition (NER) problem on informal text types for Turkish. We utilized a
semi-supervised learning approach based on neural networks. We applied a fast
unsupervised method for learning continuous representations of words in vector
space. We made use of these obtained word embeddings, together with language
independent features that are engineered to work better on informal text types,
for generating a Turkish NER system on microblog texts. We evaluated our
Turkish NER system on Twitter messages and achieved better F-score performances
than the published results of previously proposed NER systems on Turkish
tweets. Since we did not employ any language dependent features, we believe
that our method can be easily adapted to microblog texts in other
morphologically rich languages.

本文基于神经网络的半监督学习方法，利用非监督学习获取的词向量及专门为微博文本设计的语言无关特征，成功生成一种识别土耳其微博文本中命名实体的系统，并在推特信息上表现出较好的 F-score 性能，相比之前提出的 NER 系统有所提升。该方法没有使用任何特定的语言特征，因此可以轻松地适用于其他形态丰富的语言的微博文本。