## 3 Materials and Methods
### Data Preprocessing  
For data preprocessing, we use Python equipped with WordNetLemmatizer ([[https://www.nltk.org/_modules/nltk/stem/wordnet.html](https://www.nltk.org/_modules/nltk/stem/wordnet.html)]([https://www.nltk.org/_modules/nltk/stem/wordnet.html](https://www.nltk.org/_modules/nltk/stem/wordnet.html)); last accessed on 21 December 2021) for obtaining the dictionary forms of given words and Wiktionary Parser ([[https://github.com/Suyash458/WiktionaryParser](https://github.com/Suyash458/WiktionaryParser)]([https://github.com/Suyash458/WiktionaryParser](https://github.com/Suyash458/WiktionaryParser)); last accessed on 21 December 2021) for retrieving their definitions. As the outputs are different for different parts of speech, we take advantage of the POS tags already included in the datasets. These, however, have to be first mapped to the format used by Wiktionary. It is noteworthy that not all of these POS tags are accurate, which sometimes prevents the algorithm from retrieving the appropriate definition. In [PERSON] and [PERSON]'s book [51], nouns, verbs, adjectives, and adverbs are termed as the _major parts of speech_, and as such are contrasted with the _minor parts of speech_ (all the others). Alternatively, they can be called the _content words_ and the _function words_, respectively (cf. [PERSON]'s analysis [52]). The former have more specific or detailed semantic content, while the latter have a more _non-conceptual_ meaning and fulfill an essentially _grammatical_ function [53]. As a result of this, in general, definitions of the function words do not add much of semantic information that could be used in metaphor detection. On the contrary, using averaged vectors of their component tokens could become a source of unnecessary noise, leading to performance decay (consider the first definition of the very frequently occurring determiner _the_ found in Wiktionary: \"Definite grammatical article that implies necessarily that an entity it articulates is presupposed; something already mentioned, or completely specified later in that same sentence, or assumed already completely specified\"). When it comes to function words, we therefore decided to use their lemmas instead of definitions. In this regard, we have made an exception for 12 prepositions, namely: _above_, _below_, _between_, _down_, _in_, _into_, _on_, _over_, _out_, _through_, _under_, and _up_. These are all very frequent, as they all constitute a part of the stopwords from NLTK's _corpus_ package ([[https://www.nltk.org/](https://www.nltk.org/)]([https://www.nltk.org/](https://www.nltk.org/))\(\_\)modules/nltk/corpus.html; last accessed on 21 December 2021). When present in utterances, they often manifest the underlying image schemata well known from the Conceptual Metaphor Theory first popularized by [PERSON] and [PERSON] in [54] and further described in detail by other authors, for example by [PERSON] in [55]. Admittedly, the choice of the words from the outside of the major parts of speech category is subjective and could be made differently. We assumed that it is likely that the first definition available in the Wiktionary is going to be the one representing a word's _basic meaning_ and therefore we retrieve only the first of the definitions available for the given part of speech. This choice was preceded with the lecture of Wiktionary guidelines ([[https://en.wiktionary.org/wiki/Wiktionary:Style](https://en.wiktionary.org/wiki/Wiktionary:Style)]([https://en.wiktionary.org/wiki/Wiktionary:Style](https://en.wiktionary.org/wiki/Wiktionary:Style)) guide#Definitions; last accessed on 21 December 2021), where--at least for the complex entries--it is explicitly recommended to use the logical hierarchy of the word senses, meaning: _core sense at the root_. The relation between basic meanings and metaphoricity within the logical hierarchy is explained in [56] (p. 285; emphasis added):  
The logical ordering runs from core senses to subsenses.