# Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing
## 4 Identifying Metaphors
### Metaphor Identification with the Use of Word Embeddings
#### 4.4.1 Metaphor Identification Using Static Word Embeddings  
One of the first papers that uses neural networks and word embedding to identify metaphors is by [PERSON] and [PERSON] [33]. The problem of identifying a metaphor is approached as a problem of tagging i.e., sequence labeling. The authors use a multilayer perceptron in conjunction with static word embedding, word2 vec. The data set for training and testing is VUA, but the authors use only those metaphors annotated as MRW while other metaphor type tags are annotated as literal words/expressions. In the research, the authors conduct three experiments--identification only at the token level, identification at the token level with information on the type of word (Part of Speech, POS), identification at the token level with information on the type of word and word concreteness [77], and conclude that adding information about the word type does not significantly affect the result (increase in F1 score for texts from newspaper articles from 0.6352 to 0.6385) nor does adding information about word concreteness (increase in F1 score for academic texts from 0.5579 to 0.5618 when POS and word concreteness are used). Enriching linguistic word embeddings with visual ones can provide better results in identifying metaphor, as it was shown by [PERSON] et al. [34]. Using the entire Wikipedia available at the time, the authors created word embeddings using the skip-gram method. Words were previously lemmatized (Lemmatization is a technique which reduces inflected words to their root word, which is called lemma), and words that appear less than 100 times were ignored. The creation of word embeddings is done in two phases, where in the first, an embedding of individual words is created, and in the second, an embedding of phrases. To create visual embeddings, a convolutional neural network is used, trained on the ImageNet classification task, and up to 10 images are downloaded from Google for each word or phrase. The final visual representation is made by averaging the extracted vector image representation for each word or phrase. The metaphorical quality of words is determined by calculating the similarity of the words and by defining whether they belong to the same domain--if the words do not belong to the same domain and the similarity between them is small, it is assumed that it is a metaphor. The cosine similarity method is used for the calculation. In order to determine whether a phrase is metaphorical, the vector representation of the phrase is compared with the individual words of that phrase, and if the vector representation of the phrase is similar to that of the individual word, it is assumed that it is a literal expression or phrase. The method was evaluated on the MOH-X and TSV data sets, and the authors achieved the best results with the multimodal model, combining linguistic and visual embeddings (F1 score for MOH-X 0.75 and for TSV 0.79), concluding that multimodal embeddings can further improve metaphor identification results. Relying on the cosine similarity method used by [PERSON] et al. ([PERSON] et al., 2016), [PERSON] et al. ([PERSON] et al., 2017) expanded the method by showing words in dependence on other words, thus providing context. When creating word embeddings using the skip-gram method, they use a data set with an annotated metaphor. Based on the assumption that not all dimensions of the vector representation are equally important when identifying a metaphor, the cosine similarity is expanded by adding a weighting factor, and ultimately the prediction is based on the vectors obtained in this manner. They evaluate the method on the MOH-X and TSV. In the evaluation, they use two methods of word representation--the skip-gram method and vectors based on attributes ([PERSON] et al., 2016), while the best results are achieved by combining both methods of word representation with the supervised similarity method proposed in the paper--F1 0.811 for TSV and 0.699 for MOH-X.