We present models for embedding words in the context of surrounding words.
Such models, which we refer to as token embeddings, represent the
characteristics of a word that are specific to a given context, such as word
sense, syntactic category, and semantic role. We explore simple, efficient
token embedding models based on standard neural network architectures. We learn
token embeddings on a large amount of unannotated text and evaluate them as
features for part-of-speech taggers and dependency parsers trained on much
smaller amounts of annotated data. We find that predictors endowed with token
embeddings consistently outperform baseline predictors across a range of
context window and training set sizes.

我们提出了一种基于标准神经网络架构的简单，高效的令牌嵌入模型，并通过大量未注释的文本学习了令牌嵌入，评估它们作为基于少量注释的数据训练的词性标注器和依存分析器的特征，发现词嵌入在各种上下文窗口和训练集大小下都能一致优于基准预测模型。

学习将单词嵌入上下文以执行句法任务

Learning to Embed Words in Context for Syntactic Tasks

Here we study polysemy as a potential learning bias in vocabulary learning in
children. Words of low polysemy could be preferred as they reduce the
disambiguation effort for the listener. However, such preference could be a
side-effect of another bias: the preference of children for nouns in
combination with the lower polysemy of nouns with respect to other
part-of-speech categories. Our results show that mean polysemy in children
increases over time in two phases, i.e. a fast growth till the 31st month
followed by a slower tendency towards adult speech. In contrast, this evolution
is not found in adults interacting with children. This suggests that children
have a preference for non-polysemous words in their early stages of vocabulary
acquisition. Interestingly, the evolutionary pattern described above weakens
when controlling for syntactic category (noun, verb, adjective or adverb) but
it does not disappear completely, suggesting that it could result from
acombination of a standalone bias for low polysemy and a preference for nouns.

研究童年时期语言学习的偏好发现，儿童更倾向于使用低多义词汇，而名词的低多义性可能与其它偏好相结合，此现象在控制了词性后有所减弱但并未消失。