Stance detection is the task of classifying the attitude expressed in a text
towards a target such as Hillary Clinton to be "positive", negative" or
"neutral". Previous work has assumed that either the target is mentioned in the
text or that training data for every target is given. This paper considers the
more challenging version of this task, where targets are not always mentioned
and no training data is available for the test targets. We experiment with
conditional LSTM encoding, which builds a representation of the tweet that is
dependent on the target, and demonstrate that it outperforms encoding the tweet
and the target independently. Performance is improved further when the
conditional model is augmented with bidirectional encoding. We evaluate our
approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving
performance second best only to a system trained on semi-automatically labelled
tweets for the test target. When such weak supervision is added, our approach
achieves state-of-the-art results.

文章探讨基于目标的立场检测，使用条件 LSTM 编码建立 tweet 的表示并考虑了目标未提及且缺乏训练数据的情况，在 SemEval 数据集上超越了大部分方法且当加入弱监督时达到了最优效果。

双向条件编码的立场检测

Stance Detection with Bidirectional Conditional Encoding

Target-dependent sentiment classification remains a challenge: modeling the
semantic relatedness of a target with its context words in a sentence.
Different context words have different influences on determining the sentiment
polarity of a sentence towards the target. Therefore, it is desirable to
integrate the connections between target word and context words when building a
learning system. In this paper, we develop two target dependent long short-term
memory (LSTM) models, where target information is automatically taken into
account. We evaluate our methods on a benchmark dataset from Twitter. Empirical
results show that modeling sentence representation with standard LSTM does not
perform well. Incorporating target information into LSTM can significantly
boost the classification accuracy. The target-dependent LSTM models achieve
state-of-the-art performances without using syntactic parser or external
sentiment lexicons.

本研究提出了两种基于 LSTM 的目标相关情感分类模型，通过将目标词与上下文词的语义相关性进行建模，相比传统的 LSTM 模型，这种模型可以显著提高分类准确度，并取得了最新的最优性能。