Uyghur Metaphor Detection via Considering Emotional Consistency.

Metaphor detection plays an important role in tasks such as machine translation and human-machine dialogue. As more users express their opinions on products or other topics on social media through metaphorical expressions, this task is particularly especially topical. Most of the research in this field focuses on English, and there are few studies on minority languages that lack language resources and tools. Moreover, metaphorical expressions have different meanings in different language environments. We therefore established a deep neural network (DNN) framework for Uyghur metaphor detection tasks. The proposed method can focus on the multi-level semantic information of the text from word embedding, part of speech and location, which makes the feature representation more complete. We also use the emotional information of words to learn the emotional consistency features of metaphorical words and their context. A qualitative analysis further confirms the need for broader emotional information in metaphor detection. Our results indicate the performance of Uyghur metaphor detection can be effectively improved with the help of multi-attention and emotional information.