Query: Simile Recognition

title: Neural Simile Recognition with Cyclic Multitask Learning and Local Attention
abstract: Simile recognition is to detect simile sentences and to extract simile components, i.e. tenors and vehicles. It involves two subtasks: \it simile sentence classification} and \it simile component extraction} Recent work has shown that standard multitask learning is effective for Chinese simile recognition, but it is still uncertain whether the mutual effects between the subtasks have been well captured by simple parameter sharing. We propose a novel cyclic multitask learning framework for neural simile recognition, which stacks the subtasks and makes them into a loop by connecting the last to the first. It iteratively performs each subtask, taking the outputs of the previous subtask as additional inputs to the current one, so that the interdependence between the subtasks can be better explored. Extensive experiments show that our framework significantly outperforms the current state of the art model and our carefully designed baselines, and the gains are still remarkable using BERT.

title: Neural Multitask Learning for Simile Recognition
abstract: Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. Simile recognition is to recognize simile sentences and extract simile components, i.e. the tenor and the vehicle. This paper presents a study of simile recognition in Chinese. We construct an annotated corpus for this research, which consists of 11.3k sentences that contain a comparator. We propose a neural network framework for jointly optimizing three tasks: simile sentence classification, simile component extraction and language modeling. The experimental results show that the neural network based approaches can outperform all rule based and feature based baselines. Both simile sentence classification and simile component extraction can benefit from multitask learning. The former can be solved very well, while the latter is more difficult.

title: Combining Part of Speech Tags and Self Attention Mechanism for Simile Recognition
abstract: Simile recognition is to find simile sentences and extract the tenor and vehicle from these sentences. Previous works illustrate that tenors and vehicles are typically noun phrases. A word may have different part of speech (POS) labels (e.g. adjectives, adverbs, nouns, and verbs) in different sentences. It is important for the simile recognition task to identify a certain POS information for each word in a sentence. However, existing models use the same word embedding to represent a word, which cannot accurately represent the POS information of this word in different sentences. In this paper, we propose a neural network framework explicitly integrating the POS information into simile recognition task, with additional self attention mechanism to better capture long term dependencies between any two tokens in sentences. The experimental results show that our proposed models significantly outperform previous state of the art methods in the simile recognition task. We also present an analysis showing that the POS information and self attention mechanism are effective for the simile recognition task.

title: Attention Based BiLSTM Network for Chinese Simile Recognition
abstract: Simile is a figure of speech that connects two different things with a common characteristic, which is expressed as "A is like B" In this paper, we focused on the Chinese Simile recognition which means classify the sentences between the simile and non simile. In past works, models with higher classification results usually focus on the particular syntactic structures, while models with better generalization ability have lower classification accuracy. Therefore, our goal is not only to improve the classification results of the model, but also to take into account its generalization ability. We propose to use the Bidirectional LSTM network with attention mechanism to recognize the simile sentences and then obtained a higher F1 value. In order to better describe our model, we gives a detailed introduction to the bidirectional LSTM and attention mechanism works. In addition, we discussed the role of attention mechanism in this simile sentence recognition task.

title: Comparison pattern matching and creative simile recognition
abstract: Comparisons are phrases that express the likeness of two entities. They are usually marked by linguistic patterns, among which the most discussed are X is like Y and as X as Y. We propose a simple slot based dependency driven description of such patterns that refines the phrase structure approach of Niculae and Yaneva (2013) We introduce a simple similaritybased approach that proves useful for measuring the degree of figurativeness of a comparison and therefore in simile (figurative comparison) identification. We propose an evaluation method for this task on the VUAMC metaphor corpus.

title: A Simile Recognition System using a Commonsense Sensory Association Method
abstract: We propose a method for comprehending the significance of metaphorical expressions by using an intuitive sensory association method implemented on computer. The metaphors we address in this work are similes. When simile expressions are used in human conversation, it appears that the listener uses intuitive sensory associations, cultivated through experience, to recall the characteristics of the subject and the predicate and comprehend the meaning of the metaphor by replacing the predicate with another appropriate word to describe the subject. A sensory association method has been proposed that is capable of clarifying these sorts of intuitive sensory relationships between nouns and their characteristics. The sensory association method outputs the sensations and impressions that humans naturally feel in response to a given noun. In this work, we construct a simile comprehension system based on the sensory association method and seek to use it to get computers to understand similes. In this paper, we define comprehension of a simile such as "cheeks like apples" as to convert the simile into the phrase "red cheeks." The capacity to perform this conversion demonstrates the computer's understanding that the two expressions are synonymous. The results of our tests indicated an accuracy of 65.7% thus, by introducing a sensory association method we were able to exceed the accuracy achieved in a previous study.

title: Automatic Recognition of Simile Based on Sequential Model
abstract: With the deepening of the research on language modeling, automatic metaphor identification task has become a new challenge in the field of natural language processing, especially in machine translation. This paper is about the research on simile, one kind of the most frequent metaphorical way in daily life. More specifically, the automatic recognition of metaphorical sentences with obvious metaphorical components. We divided this task into two sub tasks, the first one is classifying the sentences containing the word "like" and then labelling the tagged sentences with metaphorical components labels. In this paper, we used the Random Forest classifier and Conditional Random Field model, and finally achieved excellent results in both tasks. In the task of sentence classification of metaphorical or literal, the F1 value reaches 83% For the metaphorical components labelling, the F1 value also reached 75.53% This result shows that the use of sequential model containing both classifier and sequence labeler can effectively identify the phenomenon of simile in Chinese, and mark the metaphor symbol in the sense of linguistic.

title: Evaluation of a simile recognition treatment program for hearing impaired students.
abstract: The investigation incorporated a changing criterion design to demonstrate the effectiveness of a treatment program designed to increase recognition of similes by two hearing impaired students. Results indicated that the treatment program increased the students' ability to correctly match similes and descriptive passages. Implications for practitioners and suggestions for future research are discussed.

title: Information Extraction and Calculation of Similarity for Simile Recognition

title: The bond between simile and metonymy in a rare rhetoric variety of folk literature
abstract: The combination of two categories (simile and metonymy) in folk literature leads to emergence of a new and rare rhetoric variety which has been totally ignored by Persian specialist. By presenting the rhetorical abilities of the Persian language, especially in the field of oral literature, the recognition of this rarity, its comparison with the usual similes of the Persian language and the expression of ways to share it and to differentiate it are the main objectives of this study. In this regard, the present study attempts to analyze this rhetorical form amongnst four books entitled Amsal o Hekam by Dehkhoda, twelve thousand Persian proverbs and their thirty thousand equivalents by Shakourzadeh, the slang dictionary by Amir Qoli Amini and the great dictionary of Persian proverbs of Hassan Zolfaqari to enumerate the characters of this type of simile and highlight the points of divergence and convergence. Research methodology is descriptive analytical. The statistical community includes 6451 similes extracted from the books mentioned. The volume of data, irrespective of the iterative nature of the data, is 52, referred to in this study, and analyzed as total induction using standard rhetorical techniques. The result of this study showed that oral literature has had a special aesthetic structures and principles which have neglected these cases preventing them to be better understood. Studies showed the structural difference between this rare literary type and its similar species in public literature, which has been described in detail in the article.