# Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing
## 3 Annotating Metaphors
### Annotating Linguistic Metaphors
Figure 1: Flow diagram of MIP and MIPVU, showing the overlap between them. Points 1 and 2 of MIPVU procedure are, as authors state, essentially the same as MIP, and as it is shown in Figure 1, MIPVU procedure upgrades the MIP--once a metaphor is detected, MIPVU guide us on determining the metaphor type. As we can notice, the MIPVU mentions \"cross-domain mapping\", which indicates that the metaphor needs to be considered through the conceptual metaphor theory, while [PERSON] and [PERSON] (2017) state that in order to identify direct and implicit metaphors, metaphor identification absolutely needs to be approached from the level of the conceptual metaphor. Direct metaphors are often expressed through comparisons using the preposition _like_ or _as_, which means that, if we do not apply the theory of the conceptual metaphor here, the metaphor will not be identified. The implicit metaphor, on the other hand, is expressed by pronouns and ellipsis, where the pronoun or ellipsis forms an indirect or direct meaning that can be explained by cross-domain mapping. While the MIP states that it is based on a maximalist approach, [PERSON] and [PERSON] find that, despite the MIPVU procedure giving the impression that everything could ultimately be a metaphor, practice shows that only 13.6% of the lexical items in the corpus are annotated as metaphors (The distribution is also interesting--18.5% academic texts, 16.4% newspaper articles, 11.9% fiction and 7.7% conversation. We would certainly expect a higher percentage of metaphors in fiction since conventional wisdom is that metaphors are reserved for fiction. [PERSON] (2011) also reports the highest percentage of metaphors in newspaper articles, politics, and in a language and literature journal). Also, they state that direct and implicit metaphor each make up only 0.2% of that. The authors of both procedures complicate the definition of the lexical unit--most often the lexical unit coincides with the word, but this is not always the case. For instance, in the English language, there are phrasal verbs, such as _get up, get out_, and _give up_, which must be viewed as one lexical unit. In addition to phrasal verbs, there are also phrases, compounds, expressions, and the like for which it also needs to be determined whether they are one lexical unit or whether each word should be viewed as a separate lexical unit. The MIPVU procedure recommends that all words, except phrasal verbs, compounds (e.g., _underpass, power plant_) and names be considered as one lexical unit. Both procedures state that in identifying a metaphor, i.e., in determining the basic meaning of lexical units as well as in determining the lexical units themselves, it is necessary to consult monolingual dictionaries. The MIP, for instance, uses the _Macmillan English Dictionary for Advanced Learners_ as a primary dictionary and the _Shorter Oxford English Dictionary_ as a supplementary dictionary for information about the etymology of words. As its two main dictionaries, the MIPVU uses the _Macmillan English Dictionary for Advanced Learners_ and the _Longman Dictionary of Contemporary English Online_, and additionally consults the _Oxford English Dictionary Online_. In the development of the MIPVU procedure, data were also annotated, which led to the publicly available annotated data corpus, the VU Amsterdam Metaphor Corpus (The data set is available at [[http://www.vismet.org/metcor/documentation/home.html](http://www.vismet.org/metcor/documentation/home.html)]([http://www.vismet.org/metcor/documentation/home.html](http://www.vismet.org/metcor/documentation/home.html)) (last accessed on 11 February 2023), and is referred to as VUA in the rest of this paper, as is usual in research papers and relevant literature), which contains 186,695 lexical items, annotated as metaphors (or non-metaphors). It is this data set that is most often used when developing models for machine learning and natural language processing for the purpose of identifying metaphors.