# Deep Learning Methods for Extracting Metaphorical Names of Flowers and
Plants

Amal Haddad Haddad1, Damith Premasiri2, Tharindu Ranasinghe3, Ruslan Mitkov4
1University of Granada, Spain
2University of Wolverhampton, UK
3Aston University, UK
4Lancaster University, UK
<EMAIL_ADDRESS>

##  1 Introduction

Metaphor is a pervasive phenomenon in human language ([Lakoff and Johnson,
2008]. It is defined as "mapping of conceptual structure from a source to a
target domain" ([Ruiz de Mendoza Ibáñez, 2017]. Depending on the dimension of
complexity of metaphor, authors distinguish two types of metaphors: image
metaphors and conceptual metaphors ([Lakoff and Johnson, 2008]. Image
metaphors compare one single image in one domain with another image belonging
to another domain, such as the image metaphor "she is as good as gold".
Conceptual metaphors are more complex at the conceptual and cognitive levels,
and they refer to the resemblance established between a whole set of
experiences, such as the metaphor in "life is a journey", which implies a
whole set of elements activated within the metaphoric target domain. Metaphor-
based terms, or the so-called terminological metaphors, are common in
specialised languages. Their use is abundant, as they help in the
conceptualisation of phenomena and their description by establishing a
resemblance between images and domains. They also help in understanding
abstract phenomena in terms of more concrete notions and in modelling
scientific thought ([Urena Gomez-Moreno and Faber, 2010]. However, the
identification of metaphor-based terms in discourse is an arduous task. This
leads in some cases to committing errors during translation processes and
lexicographic tasks. The process is even more challenging when it comes to
machine translation, both in the cases of single-word terms and multi-word
terms, which are represented by Multiword Expressions (MWEs). The main common
error while carrying out the translation processes is that the metaphorical
lexical items forming part of a term would be transferred literally into other
languages without taking into consideration its metaphoric and cultural
dimension or without taking into account that they form part of an MWE.

Previous studies focused on the extraction of metaphorical terms from
discourse, such as [Mu, Yannakoudakis, and Shutova, 2019] and [Razali et al.,
2022]; however, to the best of our knowledge, there are no programs that could
automatically retrieve those terms both as single-word terms and MWEs in
specialised languages. This study seeks to fill in this gap and proposes a
novel method based on transformer models ([Premasiri et al., 2022, Premasiri
and Ranasinghe, 2022]; ([Ranasinghe et al., 2021] for automatic extraction of
metaphor-based terms from the specialised domain of Botany and concerning the
names of flowers and plants in English and Spanish. The main contributions of
this study are:

  1. 1.

We empirically evaluate thirteen discriminative transformer models and one
generative transformer model (ChatGPT) for the tasks of metaphoric flower and
plant names identification on English and Spanish datasets.

  2. 2.

We show that discriminative models perform better in the metaphoric flower and
plant names identification task.

  3. 3.

We release new annotated datasets for metaphoric names identification in
English and Spanish.

  4. 4.

We make our code freely available for further
research111https://bit.ly/3pYAYXK.

This paper is organised as follows: in Section 2 we present previous related
work. In Section 3 we describe the dataset used and its annotation process. In
Section 4 we detail the experimental set-up and methodology, while in Section
5 we report our experiment’s results and evaluation. Finally, we summarise the
main conclusions and propose future work in Section 6.

##  2 Related work

The study of metaphor-based terms in discourse has been a subject of study in
the last few decades. One of the main concerns in this field is the detection
of metaphor-based words in discourse. With this aim, the Pragglejaz Group
suggested a method for the manual identification of metaphor, called Metaphor
Identification Procedure (MIP) ([Group, 2007]. This method has been used
extensively ([Nacey et al., 2019]. Studies like [Turney et al., 2011], [Jang
et al., 2015] and [Coll-Florit and Climent, 2019] have a similar approach.
Other projects such as the VU Amsterdam Metaphor Corpus ([Leong et al., 2020]
offer a manually annotated corpus for all metaphorical language use. Moreover,
studies like [Yaneva, 2016], show how the use of metaphor and figurative
language in discourse is of utmost difficulty for people with Autism Spectrum
Disorder (ASD); hence, studies like [Yaneva, 2016] and [Štajner et al., 2017]
endeavour to identify and disambiguate complex sentences which contain
metaphor and metonymy among other features through the application of Complex
Word Identification modules. The above studies were partially inspired by the
FIRST Project222http://www.iwebtech.co.uk/project-first/ ([Orăsan, Evans, and
Mitkov, 2018] and the development of the Open Book tool which helps people
with ASD.

Concurrently, one of the recent concerns of Natural Language Processing (NLP)
applications and Machine Translation (MT) technologies is the automatic
identification of metaphor-based words in discourse through Deep Learning
Methods (DLM). For example, [Mu, Yannakoudakis, and Shutova, 2019] suggest
working with large corpora and training simple gradient boosting classifiers
on representations of an utterance and its surrounding discourse learned with
a variety of document embedding methods”. [Su et al., 2020] focus on token-
level metaphor detection paradigm and propose using an end-to-end deep
metaphor detection model. Authors like [Razali et al., 2022] use machine
learning to automatically detect metaphor instances in short texts by
implementing Support Vector Machine algorithms, while other authors like
[Gutierrez et al., 2016] propose modelling metaphor explicitly within
compositional distributional semantic models to improve the resulting vector
representations. Those authors classify the already used methods in the
following categories: clustering; topic modelling; topical structure and
imageability analysis; semantic similarity graphs and feature-based
classifiers ([Gutierrez et al., 2016]. Recent approaches are more centred on
using dense embedding methods ([Vitez et al., 2022].

On the other hand, the study of metaphor-based terms in specialised discourse
has been subject to scientific and cognitive studies. The automatic
identification of metaphor-based terms is considered a substantial challenge.
Some studies highlight the importance of automatic extraction of terms in
specialised discourse ([Rodríguez Penagos and others, 2005] while other
studies, such as [Urena Gomez-Moreno and Faber, 2010], propose a semi-
automatic method for term retrieval in the domain of Marine Biology. However,
to the best of our knowledge, there have been no previous studies or
methodologies which cover the automatic extraction of those terms from
scientific discourse in other domains and no previous studies were carried out
in the domain of Botany.

##  3 Data

Specialised discourse is rich in metaphor-based terms; Botany is no exception.
The semantic motivations for plant names are usually influenced by the
appearance of the plant, the place of its occurrence, the properties of the
plant, its usage, as well as other motivations typical of a specific genus of
species ([Dębowiak and Waniakowa, 2019]. Many studies have shown that metaphor
is one of the most frequent techniques to coin flowers and plants names
([Rastall, 1996]; ([Nissan, 2014]; ([Dębowiak and Waniakowa, 2019]. This
metaphoric use may give clues to cultural references related to legends and
beliefs associated with plants in general, like their healing properties and
supposed magical powers ([Dębowiak and Waniakowa, 2019]. At the same time,
this shows that this metaphorical use may vary among languages and cultures.
From another perspective, studies like [Goodman, 1963] highlight the
importance of flower names based on metaphor for the study of colour and its
comparison among languages. For this reason, we consider the study of
metaphor-based terms in this domain relevant as a case-study.

The dataset we use to extract metaphor-based terms in English is the
Encyclopaedia of Flowers and Plants, published by the American Horticultural
Society ([Brickell, 2012]. We selected this edition as it is available in a
digitalised format in the online library of the Internet Archive. This
Encyclopaedia consists of 522,707 words. It contains a dictionary of names of
flowers from around the world, with approximately 8000 terms referring to both
scientific and common names and their origins, as well as 4000 images. It is
divided into the following sections: firstly it has an introduction about how
to use the book, plant names and origins and relevant information on how to
create a garden and how to select plants. This introductory part shows that it
is aimed at both professionals and laypersons. Secondly, it has a plant
catalogue, subdivided into categories such as trees, shrubs, roses, climbers
and wall shrubs, perennials, annuals, biennials and bedding, rock plants,
bulbs, water and bog plants as well as tender and exotic plants. All those
subsections contain rich contexts on each term, concerning the origin, uses,
habitat, size, etc. Finally, the Encyclopaedia offers a dictionary section
with an index of common names and glossary of terms. We benefited from this
last section to extract and annotate terms. The advantage of using this
Encyclopaedia is that it includes a wide range of varieties of flowers and
plants from all around the world. For this reason, the obtained results may be
useful to be applied in different contexts and in multidisciplinary studies.

The data was pre-processed by annotating the proper names and their
metaphorical condition. The MIP criteria for metaphor identification ([Group,
2007] was adapted to annotate the terms, considering a term as metaphor-based
when one or more of the lexical units forming it or its etymology give
evidence that they belong to different domains, based on its meaning in the
dictionary. The annotated names represent both image metaphors and conceptual
metaphors. An example of image metaphors, is the one-word name of the flower
Edelwiess which is a combination between the two lexical units edel which
means noble and weiss, which means white in German. This name represents an
image metaphor where the flower is called as so as it symbolises purity. The
scientific name of this flower is Leontopodium Alpinum, an MWE with Greek
origin and etymology. It is also an image metaphor, as the lexical unit
Leontopodium means lion’s foot ([Dweck, 2004], the resemblance is established
between the for of the petals of the flowers and the aspect of the foot of a
lion. Another example are the flowers Sunburst and Moonlight. The name of the
flower Sunburst shows the resemblance between the colours of the flower and
the colours of the sun, while the flower called Moonlight, alludes to the
resemblance between the flower and the light of the moon. Other metaphor
based-names represent a conceptual image, such as the MWE flower name forget-
me-not which refer to the association between the heart-shaped blue flowers
that reminds the person of his or her beloved one; or the one-word name of the
flower cascade which associate the aspect of a flower with the whole process
of the water falling in a real cascade.

Apart from the Encyclopaedia of Plants and Flowers, we also compiled a corpus
of other resources related to Botany in English. It consists of 437,663 words.
Some of the texts are monographs, others are journal articles, and some texts
are retrieved from other online resources. The full list of references used to
compile the English corpus are listed in Appendix 1. With respect to the
Spanish dataset, we have annotated a list of flowers and plants names provided
in selected monographs and glossaries following the same criteria as in the
case of the English terms. Above all, we used books and articles in the domain
of Botany and botanical glossaries, such as the glossaries provided in Los
Áraboles en España ([de Lorenzo Cáceres, 1999], Biología de la Conservación de
Plantas en Sierra Nevada ([Peñas and Lorite, 2019] and the glossary of
scientific names of plants and their vernacular names provided by the
Entomological Museum in Leon in the Bio-Nica webpage333http://www.bio-
nica.info/home/index.html. The list obtained from this source consists of more
than 5000 scientific and vernacular names of flowers and plants. As for the
book Los Áraboles en España, it consists of almost 155,000 words with more
than 600 terms in the section of Glossary. The book describes the details of
each plant, its family names, its vernacular names and synonyms, its origin,
etymology, description and cultivation information. It also provides
illustrative images of each plant. The book Biología de la Conservación de
Plantas en Sierra Nevada was also valuable as some of its chapters contained
lists of scientific names of endemic flowers from Sierra Nevada and its common
names too. In order to enhance the datasets, we also added more specialised,
semi-specialised and informative texts in the domain of botany to obtain more
rich contexts. It consists of 460,258 words. The full list of the sources used
to compile the Spanish corpus are listed in Appendix.

With this paper, we release datasets of English and Spanish flower and plant
names with their annotations metaphoric or not metaphoric. The English dataset
consists of 6330 total plant and flower names as a combination of 1869
metaphorical names and 4461 non-metaphorical names. The Spanish dataset
consists of 875 metaphoric names and 4,988 non-metaphoric names out of 5863
total.

#### Data Preparation

Since we model the metaphoric name identification task as a token level
classification task, we used IOB format tagging for our corpus. IOB format is
widely used in token level classification tasks ([Tjong Kim Sang and De
Meulder, 2003] where B - Beginning, I - Inside and O - outside of a metaphoric
flower or plant name; Table 1 shows an example IOB annotation. After tagging
the sentences from the corpus, we identified that there were a very high
number of sentences which do not have a single metaphoric name. In other
words, the majority of the sentences only had ’O’ as the tag for all their
words. Since this has a negative impact on the model training process, we
decided to balance the dataset by removing some sentences. Then we shuffled
all the sentences and divided the training and test sets. Finally, we had 2020
total sentences divided 1500 and 520 in English training and test set
respectively. For Spanish, we used only 250 sentences as the dataset.

calliandra | haematocephala | (Red | powder | puff) | is | an | evergreen, | spreading | shrub
---|---|---|---|---|---|---|---|---|---
O | O | B | I | I | O | O | O | O | O
Table 1: BIO annotation example

Test sets were the same for discriminative and generative experiments. The
only thing is that in the generative approach, we did not use the training
set, since we cannot train ChatGPT.

##  4 Methodology

#### Discriminative Models

Transformers ([Vaswani et al., 2017] have been a major breakthrough in Deep
Learning research, since they provide a robust mechanism based on attention
for the neural networks to flow information without recurrence and
convolution. This architecture has produced state-of-the-art results in many
NLP applications. With the introduction of BERT ([Devlin et al., 2019], which
employs the transformers architecture, the pre-trained large language models
have played an important role in pushing the boundaries of all NLP tasks such
as text classification ([Ranasinghe, Zampieri, and Hettiarachchi, 2019] ,
([Uyangodage, Ranasinghe, and Hettiarachchi, 2021], question answering
([Premasiri et al., 2022], text similarity ([Mitkov et al., 2023] etc. and
achieving new state-of-the-art. With this motivation, we use transformers as
our primary experimental setup and evaluate multiple pre-trained language
models. These models follow similar architectures to BERT ([Devlin et al.,
2019] while they are pre-trained on different corpora and different
objectives. Figure 1 ([Ranasinghe and Zampieri, 2021] shows the transformer
architecture we used where we input sentences which contain metaphoric flower
and plant names, then we obtain BIO tags from the output layer by adding a
softmax layer on top of the last hidden state of the deep network to classify
each token into one of I,O,B tags. We used several popular transformers based
pre-trained language models.

For the experiments on English dataset, we used the cased and uncased variants
of BERT base and BERT large versions. In order to establish the capabilities
of multilingual models, we experimented with the multilingual-bert ([Devlin et
al., 2019] model with its cased and uncased variants and xlm-roberta-base
([Conneau et al., 2020] model and xlm-roberta-large ([Conneau et al., 2020]
version. We further experimented with google/electra-base-discriminator
([Clark et al., 2020] model which is different from BERT architecture.
Finally, within these discriminative models we evaluate
allenai/scibert_scivocab_cased ([Beltagy, Lo, and Cohan, 2019] and
allenai/scibert_scivocab_uncased ([Beltagy, Lo, and Cohan, 2019] variants
which are specifically pre-trained on scientific corpora. We assume that
flower and plant names could appear in those corpora such that the model can
leverage the learning to produce better results.

Since Spanish is low in resources on metaphoric flower and plants names
corpora, we experimented zero-shot learning for Spanish on English data. We
specifically used the multilingual-bert ([Devlin et al., 2019] and xlm-roberta
([Conneau et al., 2020] for our experimental setting as these models provide
multilingual capabilities.

All the models were trained for three epochs, learning rate 4e-5 with 32
training batch size and for the hardware we used a GeForce RTX 3090 GPU.

Figure 1: Transformers architecture for token level classification Model | Precision | Recall | F1
---|---|---|---
bert-base-uncased | 92.8204 | 89.4824 | 91.0784
bert-base-cased | 93.4157 | 90.8295 | 92.0801
bert-large-uncased | 92.8424 | 90.6789 | 91.7219
bert-large-cased | 93.4157 | 90.8295 | 92.0801
bert-base-multilingual-uncased | 91.7655 | 89.6286 | 90.6648
bert-base-multilingual-cased | 93.3662 | 91.1718 | 92.2349
xlm-roberta-base | 90.1220 | 89.6020 | 89.8560
xlm-roberta-large | 90.8455 | 89.4348 | 90.1220
xlnet-base-cased | 89.8189 | 90.8769 | 90.3402
roberta-base | 91.9779 | 89.8922 | 90.9025
google/electra-base-discriminator | 92.0412 | 91.1617 | 91.5898
allenai/scibert_scivocab_uncased | 91.7084 | 90.3453 | 91.0071
allenai/scibert_scivocab_cased | 92.3408 | 90.6466 | 91.4750
ChatGPT | 62.1516 | 45.1943 | 48.1392
Table 2: Resutls for English metaphoric flower and plant names identification; the Model column represents the model we experimented, the Precision column shows the macro precision, the Recall column shows macro recall and the F1 column shows macro F1 value for the results Model | P | R | F1
---|---|---|---
bert-base-multilingual-uncased | 59.2957 | 40.3103 | 43.0472
bert-base-multilingual-cased | 54.0904 | 52.1401 | 52.8657
xlm-roberta-base | 67.4035 | 36.5622 | 37.4988
xlm-roberta-large | 64.1040 | 47.4813 | 51.8174
ChatGPT | 63.1887 | 46.6820 | 51.4120
Table 3: Results on metaphoric flower and plant names identification in
Spanish; P - The macro averaged precision, R - The macro averaged Recall, F1 -
The macro averaged F1 score.

#### Generative Models

While all above methods rely on the discriminative approach, which tries to
identify boundaries in the data space, generative models attempt to model the
placement of the data throughout the space. This approach attracted huge
attention in the research community with the release of
ChatGPT444https://chat.openai.com/ by openAI555https://openai.com/. The
research on Generative Pre-trained Transformer (GPT) ([Radford et al., 2018]
models have produced multiple versions of it including GPT-3, GPT-3.5 and
GPT-4. The free version of ChatGPT only supports GPT-3.5 for the time being
and all our experiments are based on ChatGPT free version. According to
OpenAI, the most cost capable and cost effective models out their models is
gpt-3.5-turbo, which we used to our experiments.

Since ChatGPT is a generalised conversational application, it does not
essentially provide IOB tags as outputs. After experimenting with different
prompts to retrieve IOB tags from ChatGPT, we decided it would be easier to
retrieve the metaphoric flower or plant name in the sentence from the
API666https://bit.ly/3OLCWFn and No otherwise. Prompt we used: Is there a
metaphoric flower name or metaphoric plant name included in the following
sentence, say yes or no, if yes what is the metaphoric flower or metaphoric
plant names in the sentence separately : {sentence goes here}. The outputs of
ChatGPT are not uniform, and we had to post process the outputs using regular
expressions to re-generate the IOB tags for evaluation.

Since this is a token classification task, we use macro averaged Precision,
Recall and F1 score as our evaluation metrics.

$Precision=TP/(TP+FP)$ (1) $Recall=TP/(TP+FN)$ (2)

F1 = 2 * (Precision * Recall)/(Precision + Recall)

(3)

##  5 Results and Discussion

###  5.1 English

The results in table 2 show the competitive performance of transformer models,
in the flower and plant names classification task. Despite the fact that most
of the transformer models we experimented with are not specifically pre-
trained on botanic corpora, almost all discriminative models were able to
produce more than 90% F1 score in the task. Interestingly, the multilingual
bert model could surpass the other models and mark the top results at 92.2349%
F1 score.

Another noteworthy observation in our study was that cased models outperformed
all the respective uncased models. Even though the xlm-roberta-base was the
least performer in discriminative models, the performance gap to the best
performer is only 2.3789% which shows the competitiveness of the transformers
in token level classification tasks.

Even though scibert models are specifically trained on scientific corpus,
these models were not able to outperform the bert multilingual model, which
shows that the general knowledge could play a significant role in metaphoric
identification task.

While ChatGPT seems very good at handling general text, it does not perform
well in metaphoric names identification in flower and plant names. Given that
we cannot further fine-tune the GPT model with our corpus, the ChatGPT is
struggling to identify and generate text with metaphoric flower and plant
names. Another important observation was, ChatGPT was not producing consistent
results because we could observe different results for the same sentence if we
retrieve twice. This shows that ChatGPT is uncertain about its answers on
metaphoric flower and plant names, maybe with GPT-4 it may have a better
understanding with more data. We leave it for future work.

###  5.2 Spanish

Table 3 shows the results on Spanish data in zero-shot configuration on
English data. We note that in all models, learning from English data has lead
to decent results on Spanish metaphoric flower and plant names identification.
Interestingly, bert-base-multilingual-cased model performs better in both
languages marking over 52% F1 score on Spanish. It was noted that there is a
significant difference between English and Spanish results, as expected
because the English models were fine-tuned on English metaphoric data, but we
were not able to do that in Spanish due to lack of resources.

ChatGPT has kept similar performance for Spanish recording over 51% F1 score.
This is very close value to the best discriminative model but could not
outperform bert-base-multilingual-cased model. Unlike ChatGPT, since
discriminative models are able to fine-tune, we conjecture that their
performance could be boosted with a fine-tuning step with more data.

##  6 Conclusions

The detection of metaphorical terms is an important research area for many NLP
applications. Detecting metaphor-based terms of flowers and plants may give
birth to different multidisciplinary research and applications. On the one
hand, it may help in overcoming the so-called plant awareness disparity or
plant blindness ([Parsley, 2020] as the metaphoric factor would help in
remembering the names of flowers and plants and their aspect. It may also give
insightful information to Cognitive Studies towards understanding phenomena
such as metaphor and metonymy, and even towards a more comprehensive
understanding of conceptual complexes ([Ruiz de Mendoza Ibáñez, 2017]. This
may be carried out by comprehending the associations between metaphoric names
and the image of the flower and plant representing them, and how the
resemblance of images or the metonymic aspect is conceptualised through the
coinage of terms. On the other hand, this information is also helpful for the
studies of representation of abstract phenomena in art and its comprehension
across languages. The automatic extraction of those terms is a step towards
achieving more comprehensive and accurate results. In addition, this may help
rendering texts more accessible to people with ASD. At the same time, these
types of studies may also help in the development of software or mobile
applications to be used by both laypersons and professionals.

In conclusion, we show that the state-of-the-art transformers are well capable
of performing excellently in identifying metaphoric flower and plant names.

##  7 Acknowledgements

Part of this research was carried within the framework of the projects the
projects PID2020-118369GB-I00 and A-HUM-600-UGR20, funded by the Spanish
Ministry of Science and Innovation and the Regional Government of Andalusia.
Funding was also provided by an FPU grant (FPU18/05327) given by the Spanish
Ministry of Education. We also want to thank Elvira Cámara Aguilera for her
help in the annotation process.

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##  Appendix A Appendix 1: English And Spanish Corpus

List of references used to compile the corpus in English and Spanish.

References
---
Brickell, Christopher. Encyclopedia of Plants and Flowers. New York, Dorling
Kindersley, 2012
Vigneron, Jean Pol, et al. “Optical Structure and Function of the White
Filamentary Hair Covering the Edelweiss Bracts.” Physical Review E, vol. 71,
no. 1, 19 Jan. 2005
Maghiar, Lăcrămioara M., et al. “Integrating Demography and Distribution
Modeling for the Iconic Leontopodium Alpinum Colm. In the Romanian
Carpathians.” Ecology and Evolution, vol. 11, no. 18, 25 Aug. 2021, pp.
12322–12334
Blanco-Pastor, J. L., et al. “Past and Future Demographic Dynamics of Alpine
Species: Limited Genetic Consequences despite Dramatic Range Contraction in a
Plant from the Spanish Sierra Nevada.” Molecular Ecology, vol. 22, no. 16, 12
July 2013, pp. 4177–4195
Ni, Lianghong, et al. “Migration Patterns of Gentiana Crassicaulis, an Alpine
Gentian Endemic to the Himalaya–Hengduan Mountains.” Ecology and Evolution,
vol. 12, no. 3, Mar. 2022
Pink, Alfred. Dictionary of Flowers and Plants for Gardening. Teresa Thomas
Bohannon, 2008

Table 4: English Corpora

Reference
---
Gómez García, Daniel. Flora y Vegetación de La Jacetania. De la Naturaleza.
Diputación General de Aragón, 2004
M. López Guadalupe, et al. “Comunidades, Hábitat Y Tipos de Suelos Sobre Los
Que Se Desarrolla La Manzanilla de Sierra Nevada.” Ars Pharmaceutica
(Internet), vol. 26, no. 4, 1985, pp. 255–263
Pugnaire, Francisco, et al. “Facilitación de Las Especies Almohadilladas Y
Cambio Global En Las Comunidades Alpinas Del Parque Nacional de Sierra
Nevada.” Proyectos de Investigación En Parques Nacionales: 2010-2013, by
Ministerio de Agricultura, Ministerio de Agricultura, Alimentación y Medio
Ambiente. Organismo Autónomo de Parques Nacionales, 2015, pp. 91–104
Montserrat, P, and R Balcells. “LA FLORA DEL PIRINEO.” Sinergia (Publicación
Paramédica de Sociedad General de Farmacia, SA), vol. 14, no. 6675, 1960
Paúl Gonzáles, et al. Las Plantas Comunes Del Bosque Seco Del Marañón: Biodiversidad Para Las Comunidades Locales | Bosques Andinos. Bosques Andinos, Lima- Perú, Biblioteca Nacional del Perú, 2020
Blanca, Gabriel. Flora Amenazada Endémica de Sierra Nevada. Granada,
Universidad de Granada, 2001
Peñas, Julio, and Juan Lorite. “Biología de La Conservación de Plantas En
Sierra Nevada. Principios Y Retos Para Su Preservación \- Universidad de
Granada.” Editorial.ugr.es, Universidad de Granada, 2019
Ministerio de Salud y Protección Social de Colombia. “Vademecum Colombiano de
Plantas Medicinales.” Orasconhu.org, Ministerio de Salud y Protección Social
de Colombia, 2008
Sánchez de Lorenzo Cáceres, Jose Manuel. Los Árboles En España: Manual de
Identificación. Ediciones Mundi-Prensa, 1999

Table 5: Spanish Corpora