# Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing
## 4 Identifying Metaphors
### Metaphor Identification with the Use of Word Embeddings
#### 4.4.2 Metaphor Identification Using Contextual Word Embeddings
MIP and SPV are also found as the backbone of MisNet method [49], proposed by [PERSON] et al. These two linguistic theories are combined in MisNet architecture. MIP module is used for semantic matching of the target word and the basic usage, while the goal of SPV module is to measure semantic incongruity of the target word in context. These authors emphasize that SPV can be invalid in the case of conventional metaphor as those do not have paradoxical context. In order to combine MIP and SPV, siamese framework is adopted (similar to MelBERT), where left part encodes the given sentence and right part uses the target, POS and basic usage. MIP is implemented on both, left and right encoders, while SPV is implemented only on the left one. The proposed model is validated on VUA All, VUA Verbs and MOH-X datasets and the authors report F1 score of 0.794 on VUA all, 0.759 on VUA Verb and 0.834 on MOH-X. FrameBERT [47] is RoBERTa based model that also utilizes MIP and SPV, whose authors, [PERSON] et al., hypothesized that using external knowledge of concepts is essential for improving not just metaphor identification but also explainability and to support this, they use FrameNet (FrameNet is a lexical database, accessible at [[http://framenet.icsi.berkeley.edu/](http://framenet.icsi.berkeley.edu/)]([http://framenet.icsi.berkeley.edu/](http://framenet.icsi.berkeley.edu/)); last accessed on 13 May 2023). The architecture of the FrameBERT incorporate FrameNet embeddings for concept-level metaphor detection, while by using MIP to identify the gap between contextual and literal meaning, metaphorical word is identified. With SPV, semantical difference from surrounding words points out that word is used metaphorically. Authors report F1 score of 0.788 on VUA18 dataset, 0.73 on VUA20 dataset, 0.838 for MOH-X, and 0.742 on TroFi dataset. Conceptual Metaphor Theory (CMT) has inspired [PERSON] et al. [48] to propose a model that takes as input depended word pairs (verb-noun, adjective-noun) and gives as output labels indication metaphoricity of a word pair. This model consists of three steps, first being a common association acquisition where they count the frequency of each co-occurring word in Wikipedia as well as dependency of a word pair. In the second step, conceptualization, a noun and a word pair are conceptualized with the use of WordNet and statistical point knee algorithm. In the final, third step, RoBERTa and multitask learning model learn simultaneously, to identify metaphor and to generate a concept. Model is evaluated on MOH-X, TSV, and GUT datasets, where achieved F1 score for MOH-X was 0.756, TSV 0.866, and GUT 0.925. \begin{table}
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**Paper** & **Data** & **Methodology** & **Approach** & \begin{tabular}{c} **Word** \\ **Embeddings** \\ \end{tabular}  & **F1 Score** \\ \hline \hline \multirow{4}{*}{ \begin{tabular}{c} [PERSON] et al. \\ [43] \\ \end{tabular}  } & \multirow{4}{*}{VUA, TOEFL} & \multirow{4}{*}{BERT} & Learning & \multirow{4}{*}{BERT} & VUA verbs \\  & & & from another & & 0.775, \\  & & & type of & & VUA all \\  & & & type of & BERT & 0.734, TOEFL \\  & & & figurative & & all 0.692, \\  & & & language & & TOEFL verbs \\  & & & & 0.702, \\ \hline \multirow{2}{*}{ \begin{tabular}{c} [PERSON] et al. \\ [44] \\ \end{tabular}  } & \multirow{2}{*}{VUA} & [PERSON], & [PERSON] & \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{0.715} \\  & & Hierarchical & discourse of & & 0.775, \\  & & Attention & the sentence & & 0.734, TOEFL \\ \hline \multirow{4}{*}{[PERSON] et al. [45]} & \multirow{4}{*}{ \begin{tabular}{c} TroFi, \\ MOH-X, \\ VUA \\ \end{tabular}  } & \multirow{4}{*}{CATE} & Contrastive & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{0.745, \multirow{4}{*}{VUA all 0.79, \multirow{4}{*}{VUA verbs}} \\  & & & & 0.847, \\  & & & Classification & & VUA all 0.79, \\  & & & of the target & & VUA verbs \\  & & & word & & 0.756 \\ \hline \multirow{2}{*}{ \begin{tabular}{c} [PERSON] et al. \\ [51] \\ \end{tabular}