# Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing
## 4 Identifying Metaphors
### Statistical and Machine Learning Methods  
In approaches to statistical and machine learning methods, we find two main directions--classification, i.e., supervised learning, and clustering, i.e., unsupervised learning, while in some papers the authors combine these two approaches. A brief overview of the models that utilize statistical and machine learning methods is given below, while in the Table 2 a summary of the models is presented, showing the data sets and methodology used. Based on the word sense disambiguation algorithm published by [PERSON] and [PERSON] [60], [PERSON] and [PERSON] [22] developed a TroFi system for distinguishing between literal and non-literal sentences. The authors emphasize that TroFi is not a system for metaphor (or metonymy) processing, but despite this, we mention it here because TroFi inspired later papers dealing with metaphor identification. TroFi is one of the first systems that approaches the problem of distinguishing between literal and non-literal text using a statistical method. To be more precise, it clusters literal and non-literal sentences by determining whether verbs are used literally or not. The paper achieves an F1 score of 0.538, but the result of the paper, apart from the algorithm itself, is also a TroFi data set, which contains sentences in which 50 selected verbs are used, and these sentences are in clusters--literal or non-literal. Building on the work of [PERSON] and [PERSON], [PERSON] et al. [28], using the TroFi data set to analyze metaphors expressed by verbs, create an additional data set based on adjectives, and hypothesize that the degree of abstractness of the word in context is related to the probability of how the word is used metaphorically. [PERSON] et al. represent an algorithm for calculating the abstractness of words in relation to the nouns they are related to, based on latent semantic analysis. For the classification of literal and metaphorical words, the paper uses a logistic regression, and the authors achieve an F1 score of 0.68 on the TroFi data set, which is a better result than in the original research. The Concrete Category Overlap (CCO) algorithm was developed by [PERSON] et al. [61], who took the work of [PERSON] et al. as their starting point. In the creation of the algorithm, they relied on the assumption that a metaphor is a mapping from the concrete to the abstract domain. The CCO algorithm is further expanded by [PERSON] idea of selectional preferences, i.e., by identifying whether a violation of selectional preferences has occurred. The focus of the paper is on metaphors that contain a noun, which the authors base on the work of [PERSON] and [PERSON] [59] and take over their categorization of three types of metaphors. The method presented by [PERSON] et al. was evaluated on a small set of data--the authors took five concepts--governance, government, God, mother, father--which they extracted from Reuters RCFI dataset and the New York Times archive and annotated metaphors. Their system achieves a precision of 0.72 and a recall of 0.80. In their paper, [PERSON] et al. [23] also used the TroFi data set (to identify metaphors expressed by verbs), and for the purposes of the research created an additional data set (TSV) that contains 884 metaphorical expressions where the metaphor is expressed by an adjective and a corresponding noun. On the basis of previous work, these authors [62], present the hypothesis that metaphors are primarily conceptual and only then lexical, which they demonstrate by training the model on an English data set, but the same model with a high F1 score identifies metaphors in Russian (0.84 for metaphors expressed by verbs, 0.77 for the set of adjectives and nouns), Spanish (0.76 for metaphors expressed by verbs, 0.72 for the set of adjectives and nouns) and Farsi (0.75 for metaphors expressed by verbs, 0.74 for the set of adjectives and nouns). [PERSON] et al.