People associate affective meanings to words - "death" is scary and sad while
"party" is connotated with surprise and joy. This raises the question if the
association is purely a product of the learned affective imports inherent to
semantic meanings, or is also an effect of other features of words, e.g.,
morphological and phonological patterns. We approach this question with an
annotation-based analysis leveraging nonsense words. Specifically, we conduct a
best-worst scaling crowdsourcing study in which participants assign intensity
scores for joy, sadness, anger, disgust, fear, and surprise to 272 non-sense
words and, for comparison of the results to previous work, to 68 real words.
Based on this resource, we develop character-level and phonology-based
intensity regressors. We evaluate them on both nonsense words and real words
(making use of the NRC emotion intensity lexicon of 7493 words), across six
emotion categories. The analysis of our data reveals that some phonetic
patterns show clear differences between emotion intensities. For instance, s as
a first phoneme contributes to joy, sh to surprise, p as last phoneme more to
disgust than to anger and fear. In the modelling experiments, a regressor
trained on real words from the NRC emotion intensity lexicon shows a higher
performance (r = 0.17) than regressors that aim at learning the emotion
connotation purely from nonsense words. We conclude that humans do associate
affective meaning to words based on surface patterns, but also based on
similarities to existing words ("juy" to "joy", or "flike" to "like").

研究了人们是如何从语音和词素等表层模式出发，分配词语情感强度值的，并发现人们不仅根据表面模式，而且根据词语间的相似性，如 “juy” 与 “joy” 或 “flike” 与 “like”，将情感意义赋予词语。

splink” 是愉快的，“phrouth” 是可怕的：无意义词汇的情感强度分析

"splink" is happy and "phrouth" is scary: Emotion Intensity Analysis for Nonsense Words

Recent advances in information extraction have motivated the automatic
construction of huge Knowledge Graphs (KGs) by mining from large-scale text
corpus. However, noisy facts are unavoidably introduced into KGs that could be
caused by automatic extraction. To validate the correctness of facts (i.e.,
triplets) inside a KG, one possible approach is to map the triplets into vector
representations by capturing the semantic meanings of facts. Although many
representation learning approaches have been developed for knowledge graphs,
these methods are not effective for validation. They usually assume that facts
are correct, and thus may overfit noisy facts and fail to detect such facts.
Towards effective KG validation, we propose to leverage an external
human-curated KG as auxiliary information source to help detect the errors in a
target KG. The external KG is built upon human-curated knowledge repositories
and tends to have high precision. On the other hand, although the target KG
built by information extraction from texts has low precision, it can cover new
or domain-specific facts that are not in any human-curated repositories. To
tackle this challenging task, we propose a cross-graph representation learning
framework, i.e., CrossVal, which can leverage an external KG to validate the
facts in the target KG efficiently. This is achieved by embedding triplets
based on their semantic meanings, drawing cross-KG negative samples and
estimating a confidence score for each triplet based on its degree of
correctness. We evaluate the proposed framework on datasets across different
domains. Experimental results show that the proposed framework achieves the
best performance compared with the state-of-the-art methods on large-scale KGs.

利用知识图谱中概念语义信息的交叉表示学习框架 CrossVal，通过嵌入三元组、绘制交叉负样本及估计每个三元组的正确性程度来有效验证知识图谱中的三元组。

跨图表示学习实现高效知识图谱验证

Efficient Knowledge Graph Validation via Cross-Graph Representation  Learning

Solving math word problems is a challenging task that requires accurate
natural language understanding to bridge natural language texts and math
expressions. Motivated by the intuition about how human generates the equations
given the problem texts, this paper presents a neural approach to automatically
solve math word problems by operating symbols according to their semantic
meanings in texts. This paper views the process of generating equation as a
bridge between the semantic world and the symbolic world, where the proposed
neural math solver is based on an encoder-decoder framework. In the proposed
model, the encoder is designed to understand the semantics of problems, and the
decoder focuses on tracking semantic meanings of the generated symbols and then
deciding which symbol to generate next. The preliminary experiments are
conducted in a dataset Math23K, and our model significantly outperforms both
the state-of-the-art single model and the best non-retrieval-based model over
about 10% accuracy, demonstrating the effectiveness of bridging the symbolic
and semantic worlds from math word problems.

本文提出了一个神经网络模型，基于编码器 - 解码器框架，利用自然语言理解桥接语义世界和符号世界，自动解决数学应用问题，并在 Math23K 数据集上验证模型的有效性。