This study investigates whether division on political topics is mapped with
the distinctive patterns of language use. We collect a total 145,832 Reddit
comments on the abortion debate and explore the languages of subreddit
communities r/prolife and r/prochoice. With consideration of the Moral
Foundations Theory, we examine lexical patterns in three ways. First, we
compute proportional frequencies of lexical items from the Moral Foundations
Dictionary in order to make inferences about each group's moral considerations
when forming arguments for and against abortion. We then create n-gram models
to reveal frequent collocations from each stance group and better understand
how commonly used words are patterned in their linguistic context and in
relation to morality values. Finally, we use Latent Dirichlet Allocation to
identify underlying topical structures in the corpus data. Results show that
the use of morality words is mapped with the stances on abortion.

研究考察了政治话题上的分歧是否与语言使用的独特模式有关，收集了 Reddit 上关于堕胎辩论的总共 145,832 条评论，分析了 r/prolife 和 r/prochoice 的子版块社群中的语言使用。通过考虑道德基础理论，分析了三种词汇模式，包括计算道德基础词典中词汇项目的比例频率，创建 n-gram 模型以揭示各立场群体常用的词汇搭配，以及使用潜在狄利克雷分配来识别语料库数据中的主题结构。研究结果表明，道德词汇的使用与对堕胎的立场相关联。

极化与道德：对 Reddit 关于堕胎话语的词汇分析

Polarization and Morality: Lexical Analysis of Abortion Discourse on  Reddit

We study the role of linguistic context in predicting quantifiers (`few',
`all'). We collect crowdsourced data from human participants and test various
models in a local (single-sentence) and a global context (multi-sentence)
condition. Models significantly out-perform humans in the former setting and
are only slightly better in the latter. While human performance improves with
more linguistic context (especially on proportional quantifiers), model
performance suffers. Models are very effective in exploiting lexical and
morpho-syntactic patterns; humans are better at genuinely understanding the
meaning of the (global) context.

研究语言环境对于预测量词 (`few',`all') 的作用。从人类参与者中收集众包数据，在单句和多句的情况下测试不同模型，发现模型在前者方面的表现显著优于人类，而在后者方面则仅略胜一筹。模型非常有效地利用了词汇和形态句法模式，而人类则更擅长真正理解（全局）语境的含义。

探索语言上下文对量词预测的影响

Some of Them Can be Guessed! Exploring the Effect of Linguistic Context  in Predicting Quantifiers

Learning representations for semantic relations is important for various
tasks such as analogy detection, relational search, and relation
classification. Although there have been several proposals for learning
representations for individual words, learning word representations that
explicitly capture the semantic relations between words remains under
developed. We propose an unsupervised method for learning vector
representations for words such that the learnt representations are sensitive to
the semantic relations that exist between two words. First, we extract lexical
patterns from the co-occurrence contexts of two words in a corpus to represent
the semantic relations that exist between those two words. Second, we represent
a lexical pattern as the weighted sum of the representations of the words that
co-occur with that lexical pattern. Third, we train a binary classifier to
detect relationally similar vs. non-similar lexical pattern pairs. The proposed
method is unsupervised in the sense that the lexical pattern pairs we use as
train data are automatically sampled from a corpus, without requiring any
manual intervention. Our proposed method statistically significantly
outperforms the current state-of-the-art word representations on three
benchmark datasets for proportional analogy detection, demonstrating its
ability to accurately capture the semantic relations among words.

通过提取语料库中词汇的共现模式来实现无监督学习，学习不仅代表个体单词的表示，还明确捕捉代表单词间语义关联的词向量表示，并通过联合权重和二元分类器来描述单词间的语义关系，取得了普遍类比检测三个基准数据集中的显著性优势。