U yghur Metaphor Detection Via Considering Emotional Consistency (2021)

Abstract: 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 socialmedia 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 thatlack language resources and tools. Moreover metaphorical expressions have different meaningsin 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. Ourresults indicate the performance of Uyghur metaphor detection can be effectively improved withthe help of multi-attention and emotional information.

## introduction with the rapid development of digital technology and the internet, social media has become a powerful platform where people can express their opinions on various topics such as politics, finance, education, and other social issues. it is worth noting that more users use a lot of metaphors in online texts to express their thoughts and emotions. according to statistical research, metaphors appear in every three sentences in natural language @xcite @xcite . metaphor involves not only language expression, but also the cognitive process of conceptual knowledge @xcite . according to lakoff and johnson, metaphor is a conceptual mapping. more specifically, metaphor is a concept used to describe another concept. they are widely used in oral and written language to convey rich linguistic and emotional information. for instance, in the metaphorical utterance: knowledge is treasure., we use treasure to describe knowledge to emphasize that knowledge can be valuable. to take another metaphorical instance as an example: this is an ocean of flowers. ocean has broad characteristics, which means that the flower area is large. metaphor detection is an important subtask of natural language processing, which provides a more complete representation for semantic analysis @xcite . moreover, interpreting metaphors helps to improve the performance of tasks such as machine translation and human-machine dialogue analysis @xcite . the existing computational models of metaphor detection are mainly based on lexicons @xcite and supervision methods @xcite @xcite . lexicon-based methods do not require data annotation, but they cannot detect new metaphoric usage and capture contextual information. the supervised method can obtain text context information from the sentence level, thereby obtaining broader semantic information. however, it requires a complete annotated corpus. current metaphor detection mostly focuses on english because of its abundant annotation data. the popular metaphor detection corpus in current research includes vu amsterdam metaphor corpus (vua) @xcite , moh-x @xcite and trofi @xcite . in addition, the task of metaphor detection relies on specific semantic resources, and corpora of other languages are gradually established, such as chinese metaphor corpus @xcite , russian metaphor corpus @xcite , and arabic metaphor corpus @xcite . the establishment of these corpora provides a data foundation for metaphor detection tasks in specific languages. in recent years, more uyghur-speaking users have expressed their thoughts and opinions on political, economic, and cultural topics through internet channels. as more people like to express their opinions in metaphorical language, uyghur metaphor data is also growing rapidly. the expression and meaning of metaphor differ greatly in different language environments. due to the influence of the special language environment, the task of metaphor detection in uyghur language has not yet started. therefore, it is imperative to establish uyghur metaphor detection resources and verify the effectiveness of metaphor detection models. in this paper, we have collected uyghur metaphor data from multiple fields and proposed to use multiattention mechanism for metaphor detection. more specifically, we use word embedding, part-of-speech (pos) and position as model inputs, which reveal the semantic information of the text from three levels to make the feature expression more complete. in addition, due to the important relationship between metaphor and emotional expression, we extract emotional features from both the word level and sentence level, which enables the model to learn emotional consistency information. the experimental results also verify that metaphor detection requires extensive emotional information. in this task, we treat the uyghur metaphor detection as a sequence tagging problem, and sequence tagging tasks such as pos tagging and named entity recognition (ner) have always existed in natural language processing. to summarize, this paper makes the following contributions: the rest of this paper is organized as follows. the next section introduces related work. section 3 describes our proposed model for uyghur metaphor detection. section 4 presents our experiments, including the dataset, experimental details, result analysis. the section 5 is about the conclusion. ## our proposed model in this section, we first introduce the research ideas of the paper. then, in order to describe the flow of the method in detail, we will explain each function of the model from bottom to top. ## experiment the proposed multi-attention model is applied to the task of uyghur metaphor detection. specifically, we reveal the richer semantic information of words from pos and position respectively, which makes the semantic expression of words and its context more complete. besides, due to the close relationship between metaphorical expression and emotion, we propose that emotion embedding allows the model to learn emotion consistency information during the training process. ## conclusion this paper proposed a uyghur metaphor detection model that combines multi-attention and emotional embedding. we used pos attention and position attention to construct multi-attention representations of words, which allows the model to reveal text semantics from multiple aspects. in addition, due to the correlation between metaphor and emotional expression, we construct an emotionally related dictionary as the source of emotional embedding. emotional embedding is utilized to learn the emotional consistency features of words and its context. the experimental results verify the effectiveness of the model for uyghur metaphor detection.