# Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing
## 4 Identifying Metaphors
### Statistical and Machine Learning Methods
use the random forest method for the classification, defining three main categories of features: (1) abstractness and imageability, inspired by previous papers such as [PERSON] et al. [28]; (2) supersenses categories from WordNet and (3) word embedding, which makes this one of the first papers on metaphor identification in which word embedding is used. For the TroFi data set, that is, for the metaphor expressed by verbs, [PERSON] et al. achieved an accuracy of 0.82 and an F1 score of 0.79, while for the data set created as part of the paper and where the metaphor is expressed by an adjective, they achieved an accuracy of 0.86 and an F1 score of 0.85. Following their previous work [63], [PERSON] and [PERSON] [32] approach the distinction between literal and non-literal phrases by combining unsupervised and supervised learning, where the support vector machine is used for supervised learning and classification. In the first step, unsupervised learning on the basis of lexical cohesion is used to classify literal versus non-literal expressions, and in this way an annotated data set is created, which is submitted to the classification model in the second step. The dataset consists of 17 phrases for which a five-paragraph context has been extracted from the Gigaword corpus, to help determine whether the phrase is being used literally or not. In the paper, the authors state that their model achieves an accuracy of 0.9 in distinguishing literal and non-literal phrases. An approach to metaphor identification using the method of unsupervised learning, i.e., clustering can also be found at [PERSON] et al. [30]. They start with a small seed set of data and, by clustering nouns, create a set of data that belongs to the same domain, based on the hypothesis of \"clustering by association\"--abstract concepts will be clustered into the same clusters if they are connected to the same domain, while concrete concepts will be clustered according to similarity in meaning. They use the subset of the British National Corpus, in which the metaphor is annotated, as a seed set while evaluating the method itself on the entire British National Corpus. They compared the results with the annotations made by 5 annotators and concluded that their method achieved a precision of 0.79. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Data Set** & **Methodology** & **Results** \\ \hline [PERSON] and [PERSON] [22] & TroFi & Clustering & F1 0.538 \\ \hline \multirow{3}{*}{[PERSON] et al. [28]} & \multirow{3}{*}{TroFi} & Calculating abstractness, classification & F1 0.68 \\  & & & \\ \cline{3-4}  & & Concrete Category & \\ \multirow{3}{*}{[PERSON] et al. [61]} & Five concepts & Overlap (CCO) & \\  & extracted from the & & algorithm, & 0.72 \\ \cline{1-1}  & Reuters and New York & & \\ \cline{1-1}  & Times archive & & \\ \hline \hline \multirow{3}{*}{[PERSON] et al. [23]} & TroFi, & Classification, word embedding & F1 for TroFi 0.79, 0.85 \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Overview of metaphor identification models that use statistical and machine learning methods. Another unsupervised learning approach is that by [PERSON] et al. (2018). In their paper, the authors use the Latent Dirichlet Allocation (LDA) method for topic modeling and identify metaphors in English and Spanish language texts from the governance domain. The texts were taken from newspaper portals and blogs. The hypothesis of the paper is that metaphors are contained in those sentences that contain both the original and the target concept. In order to confirm the hypothesis, the authors create a list of 60 original concepts, namely primary concepts.