This paper presents a joint model for performing unsupervised morphological
analysis on words, and learning a character-level composition function from
morphemes to word embeddings. Our model splits individual words into segments,
and weights each segment according to its ability to predict context words. Our
morphological analysis is comparable to dedicated morphological analyzers at
the task of morpheme boundary recovery, and also performs better than
word-based embedding models at the task of syntactic analogy answering.
Finally, we show that incorporating morphology explicitly into character-level
models help them produce embeddings for unseen words which correlate better
with human judgments.

本文介绍了一种联合模型，能够对单词进行无监督的形态分析，并学习从形态素到单词嵌入的字符级组成函数。该模型对单词进行分割，并根据其预测上下文单词的能力对每个分割部分进行加权。我们的形态分析与专门的形态分析器相当，并且在语法类比回答任务中表现优异。最后，我们表明，将形态学明确纳入字符级模型有助于它们生成与人类判断更相关的未知单词的嵌入。