As the key to sentiment analysis, sentiment composition considers the
classification of a constituent via classifications of its contained
sub-constituents and rules operated on them. Such compositionality has been
widely studied previously in the form of hierarchical trees including untagged
and sentiment ones, which are intrinsically suboptimal in our view. To address
this, we propose semantic tree, a new tree form capable of interpreting the
sentiment composition in a principled way. Semantic tree is a derivation of a
context-free grammar (CFG) describing the specific composition rules on
difference semantic roles, which is designed carefully following previous
linguistic conclusions. However, semantic tree is a latent variable since there
is no its annotation in regular datasets. Thus, in our method, it is
marginalized out via inside algorithm and learned to optimize the
classification performance. Quantitative and qualitative results demonstrate
that our method not only achieves better or competitive results compared to
baselines in the setting of regular and domain adaptation classification, and
also generates plausible tree explanations.

情感分析的关键，情感组合，通过对其包含的子元素的分类和对它们进行的规则操作进行分类。我们提出了语义树，一种能够以一种原则性方式解释情感组合的新树形式。语义树是上下文无关语法 (CFG) 的派生物，描述了不同语义角色的特定组合规则，这些规则是根据以前的语言学结论进行精心设计的。然而，语义树是一个潜在变量，因为常规数据集中没有它的注释。因此，在我们的方法中，通过内部算法将其边缘化并学习以优化分类性能。定量和定性结果表明，我们的方法在常规和领域适应分类设置中不仅实现了与基准方法更好或具有竞争力的结果，而且还生成了可行的树解释。

利用潜在语义树解释情感组合

Interpreting Sentiment Composition with Latent Semantic Tree

We propose methods that enable efficient hierarchical classification in
parallel. Our methods transform a batch of classification scores and labels,
corresponding to given nodes in a semantic tree, to scores and labels
corresponding to all nodes in the ancestral paths going down the tree to every
given node, relying only on tensor operations that execute efficiently on
hardware accelerators. We implement our methods and test them on current
hardware accelerators with a tree incorporating all English-language synsets in
WordNet 3.0, spanning 117,659 classes in 20 levels of depth. We transform
batches of scores and labels to their respective ancestral paths, incurring
negligible computation and consuming only a fixed 0.04GB of memory over the
footprint of data.

提出一种基于张量运算在硬件加速器上进行高效分层分类的方法，通过将给定语义树节点上的分类得分和标签批次转换为其沿着树向下的所有祖先路径上得分和标签，从而将所有标签转换为其所有祖先路径上的标签，其测试结果为在包含 20 级深度的 117,659 个分类的 WordNet 3.0 的树上，仅占用固定的 0.04GB 内存，计算量很小。