Texylon: Dataset of Log-to-Description and
Description-to-Log Generation
for Text Analytics Tools
Masato Nakata, Kosuke Morita, Hirotaka Kameko, and Shinsuke Mori
Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 Japan
nakata.masato.26m@st.kyoto-u.ac.jp

Abstract. We propose two tasks for text analytics tools, description
generation and log estimation, and a dataset to solve them. Text analytics tools, popular in the digital humanities, provide researchers withvarious functions for texts based on natural language processing (NLP).
Our ﬁrst task, description generation from operational logs, helps theseresearchers write papers easily and accurately. Our second task, log estimation given a paper, is just the opposite and helps readers reproduce
the analyses in the paper. For those tasks, we created a dataset consisting of descriptions and logs in text analytics experiments. Because ourdataset is not large enough, we also propose some methods for data augmentation: swapping a value of each conﬁguration with another value,
pseudo-labeling using BERT and NER, pseudo-labeling using BERT and
T5, and a combination of them. The highest BLEU score for that modelin the description generation task is 36.98, and F1 score in the log generation task is around 0.7.
Keywords: Data-to-text, Text-to-data, Text analytics, Natural language
processing, Digital humanities
1

1

Introduction

The uses of informatics have been getting more and more common for the humanities in these decades [3, inter alia]. Among them one of the most typical andclassical usage is statistical analysis of texts, such as term frequencies/document
frequencies of words, co-occurrence network, correspondence analysis for word
distribution, and cluster analysis. A text analytics tool, Voyant Tools2 , has recently been awarded by Alliance of Digital Humanities Organizations, for example. In addition to such web-based tools, installation type tools are also available. Famous one is KH Coder [7,8], which provides many powerful functions
1

This preprint has not undergone peer review (when applicable) or any postsubmission improvements or corrections. The Version of Record of this contribution
will be published in New Frontiers in Artiﬁcial Intelligence, Springer.
2
https://voyant-tools.org/ accessed on 2023/Oct/20.

Fig. 1. Bidirectional conversion between operation logs and descriptions.

to analyze texts in various domains, including literature, historical documents,
questionnaires, etc.
One of the faults in many analytics tools is that conﬁgurations are a bitcomplicated for non-specialists in statistics, so that describing what the conﬁgurations are appropriately can be hard work. Such a diﬃculty implies another problem: inappropriate descriptions result in the low reproducibility forreaders. A proper description for a general-purpose text analytics tool needs
authors/readers to have statistical knowledge.
In this background, we aim to build a model that
1. generates an appropriate description given a set of conﬁgurations to lighten
the burden on the authors’ side, and2. estimates underlying conﬁgurations given a description to help readers reproduce the results;
however, no proceeding datasets have been constructed for those tasks, so thistime we announce the ﬁrst dataset, Texylon, which can be used for description/log generation tasks. The precise deﬁnitions of a “description” and a “log”
are given in §3, but roughly speaking, a description is a part of a paper where theauthors explain which operation they conducted and with which conﬁgurations.
A log corresponds to the conﬁgurations for that operation.
One drawback of our dataset is the modest size nevertheless, as it is the ﬁrst
dataset in this domain. To relieve that problem, the second subject of our paper
has appeared now — we had better augment the dataset in order to predict
descriptions/logs more precisely and stably.
In this paper, ﬁrst we deﬁne the tasks because we use confusing terms to namesimilar concepts, then introduce our dataset. We also present several methods
for augmenting the data. In particular, some methods require speciﬁc language
models, so we will also describe the construction of such models. Then, we will
go on to the evaluation of the augmentation methods by proposing a baselinemodel for the log-to-description and description-to-log tasks.

2

Related WorkHe et al. studied eﬀective and eﬃcient usages of software logs in a statisticalor machine learning way [6]. They presented a survey on some topics, including
the design of logging systems, log compression and parsing logs into structured
data. In their paper, the deﬁnition of a log is closer to ours of a “description”(see §3), and structured data means our “logs.”
They aimed for more reliable engineering. Although their approaches looked
good for engineering, where a log is always formatted and well-structured, they
do not work for natural language tasks.
Nivikova et al. published E2E, a dataset for natural language generation in
the restaurant domain [12]. It contains a large amount of meaning representations(MRs) of restaurants and reference texts that explain about the restaurants innatural language.
Kale et al. proposed a method which can be applied to general data-to-text
generation tasks [9]. They used pre-trained T5 models [15], the large language
models (LLMs) of a wide range of application, and then ﬁne-tuned them on a few
shot data representing the structured data. For example, they tried ToTTo [14],which consists of Wikipedia tables paired with natural language descriptions, asa ﬁne-tuning dataset.

3

Task Deﬁnitions

The tasks we introduce in this paper are the following two:
1. Log-to-description: description generation from operational logs, whichhelps text analytics researchers write papers easily and accurately,
2. Description-to-log: operational log estimation from descriptions, whichhelps paper readers reproduce the analyses.
Here, by an “operational log” we mean a pair composed of the following data:– a function name — which function(s) is used in a research;– conﬁgurations — key-value pairs that represent the parameters to this function(s).
To formulate the two tasks in a stricter manner, we deﬁne a few sets asfollows. Let FT be a set of all the functions in a text analytics tool T . For eachfunction f ∈ FT , we have a set of keys of its conﬁgurations, denoted by KT (f ),and a set of possible values for a key k ∈ KT (f ), denoted by VT (f, k). Then, aconﬁguration of f is no more than an ordered pair (k, v), where k ∈ KT (f ) andv ∈ VT (f, k).
⨿
Before we describe about conﬁgurations, remember that the symbol
represents a disjoint union of a family of sets:⨿(1)
Xλ := {(λ, x) | λ ∈ Λ, x ∈ Xλ }λ∈Λfor a family of sets Xλ indexed by λ ∈ Λ. Using this symbol, the disjoint unionof VT (f, k) running k ∈ KT (f ) is a set⨿(2)
VT (f ) :=VT (f, k) = {(k, v) | k ∈ KT (f ), v ∈ VT (f, k)}.
k∈KT (f )Now we can deﬁne conﬁgurations of f as a map c : K → VT (f ) for somesubset K ⊂ KT (f ) with a property c(k) ∈ {k} × VT (f, k). A condition for a keyk ∈ KT (f ) being not contained in K implies that the key k is not mentioned
in a paper and its value appears set to the default value. We denote the set of
conﬁgurations of f by
CT (f ) := {conﬁgurations of f },and an operational log, or simply a log, is an element of the set⨿
LT :=CT (f ) = {(f, c) | f ∈ FT , c ∈ CT (f )}.

(3)

(4)

f ∈FT

Let us provide an example situation here. KH Coder [7,8] has a function “cooccurrence network,” which is useful for the visual analysis of the word statistics.
We denote it by CN ∈ FKC (KC stands for “KH Coder”). For this function CN,
the key set KKC (CN) contains, for example, Pos := “target parts of speech” andMinApp := “the minimum number of appearances of a word.” The value sets may
include these instances:“noun and verb” ∈ VKC (CN, Pos),
50 ∈ VKC (CN, MinApp).

(5)
(6)

If we deﬁne a map c : {Pos, MinApp} → VKC (CN) by Pos 7→ “noun and verb,”
MinApp 7→ 50, we obtain conﬁgurations c ∈ CKC (CN). Suppose that we have conducted an experiment expressed by the above operational log (CN, c), and that
we are about to write a paper. In this case, the “description” may be fully descriptive sentences like “We calculated the co-occurrence network by KH Coder. To
this end, we set the target parts of speech to ’noun and verb’, and the minimum
number of appearances of a word to 50.”
With these notations, our tasks can be formulated in this way:
1. Log-to-description. Given an operation log ℓ ∈ LT , output a descriptiond which maximizes the probability p(d | ℓ);
2. Description-to-log. Given a description d, output an operational log ℓ ∈LT which maximizes the probability p(ℓ | d).

4

DatasetTo evaluate methods for the tasks and even train methods based on machinelearning techniques, we constructed a dataset, which we call Texylon. Texylon

Table 1. Dataset size of the manual annotation and the data augmentations.
Dataset Type # of logs # of newly added
Texylon253–
Swapping
1,873(+ 1,620)
BERT-NER
3,592(+ 3,339)
BERT-T5
14,375(+14,122)
Hybrid5,212(+ 4,959)

consists of manually annotated description-log pairs (d, ℓ)’s, where d is a description fetched from a real-world paper and ℓ is its operational log ∈ LT for some
text-anatytics tool T .
In this section we explain our dataset, Texylon, and then we propose fouraugmentation methods. Table 1 summarizes the speciﬁcations of Texylon andthe augmentation results.

4.1

Data Construction

We took KH Coder [7,8] as the example of text analytics tools. We ﬁrst collected
papers written in Japanese published in 2021 in the list of its oﬃcial web page3 .
Then annotators located the descriptions about the operations of the tool, and,given such descriptions, the annotators created their operational logs manually.
Of course, there can be a potential bias to focus on the only one text analytics
tool; however, descriptions obtained should be similar among all the tools, so ourdata augmentation methods (§4.2) and a model (§5.1) are applicable beyond KH
Coder.
The format of the logs is in a python style like FUNCTION(KEY1=VALUE1, ...).
The following is an example.
CN(Pos="noun and verb", MinApp=50)
Here, Pos and MinApp ∈ KKC (CN), represent the keys deﬁned in §3.
Since almost all the researches in the selected papers focused on co-occurrence
network or correspondence analysis, we selected only the papers which conductedco-occurrence network or correspondence analysis as our ﬁrst attempt.
The possible conﬁgurations CKC (f ) of each function f ∈ FKC were known inadvance at the annotation time, so it had little cost for determining the rigorous
annotation standard.
Note that the above annotation is not ambiguous for human, we did not
ask multiple annotators to work on the same part in order to calculate interannotator agreement.
3

https://khcoder.net/bib.html accessed on 2023/Oct/19.

Fig. 2. Swapping, replacement of a value with another in a manually annotated dataset.

4.2

Data Augmentation

Since our task is novel and hence the size of our dataset is not large enoughfor machine learning, we propose four methods for augmenting the data. In thesubsequent part, we explain these one by one.

Swapping The ﬁrst method of data augmentation is swapping [4]. Swapping
is a simple algorithm that replaces values in conﬁgurations by diﬀerent existingvalues (Figure 2).
More precisely, we created ﬁrst the list of all the conﬁgurations obtained
from Texylon (§4.1) for a function f (co-occurrence network or correspondence
analysis). Call this list Lann (f ) ⊂ CKC (f ). Next, for each paper and each function
f that is used there, let its conﬁgurations be c : K → VKC (f ) for some K ⊂KKC (f ). For each key k ∈ K, we randomly selected a new value vk′ ∈ VKC (f, k)from the list Lann (f ) which belonged to the same key k. Then we collected the
new key-value pairs {(k, vk′ )}k∈K , and we got the new conﬁgurations c′ : k 7→
(k, vk′ ). We repeated this operation 1,000 times per conﬁgurations c, until we
add 20 new conﬁgurations.
We made new corresponding descriptions as well by simple pattern matching. Because diﬀerent keys have diﬀerent values (VKC (f, k) ∩ VKC (f, k ′ ) = ∅)in Texylon, a simple replacement was enough to produce the new descriptions
unambiguously.

Pseudo-labeling Pseudo-labeling is the second method to enlarge our dataset.
Here we suggest two ways for pseudo-labeling: BERT-NER and BERT-T5, as
discussed below.
We prepared target texts to pseudo-label by downloading all the papers of
KH Coder experiments found in the oﬃcial list (as mentioned in §4.1) on October
11th, 2022. Note that Texylon was made from the papers in 2021; therefore we
excluded the year 2021 from those target texts.

Fig. 3. Pseudo-labeling: BERT classiﬁes functions and NER or T5 extract conﬁgurations.

BERT-NER BERT is a transformer-based language representation model proposed by Devlin et al. [5] We used BERT to classify functions (∈ FKC ) used in
papers: i) co-occurrence network, ii) correspondence analysis, and iii) others (seeFigure 3). We adopted a BERT model pre-trained on Japanese Wikipedia articles4 , and then it was ﬁne-tuned on Texylon, so that it can predict the function
given a description.
On the other hand, we predicted conﬁgurations by named entity recognition (NER). Before applying NER, we tokenized the descriptions in Texylon
into words with a Japanese morphological analyzer, MeCab [11]. For annotating
NER labels, we preset 19 types of named entity (NE) specially for this task, eachof which corresponds to each key in the possible conﬁgurations. Then, we annotated the values word-by-word in the IOB2 (inside-outside-beginning) format.
For example, a label B-PART means the “beginning of a value” for the conﬁguration 分析対象の品詞 (bunseki taishou no hinshi, target parts of speech), and alabel I-MINAPP indicates that a word is “inside a value” for the conﬁguration 語の最小出現数 (go no saishou shutsugen suu, the minimum number of appearancesof a word).
After annotating, the NER model was trained via the Flair framework [1].
We utilized the Japanese word embeddings provided with Flair by default. Furthermore, we eliminated the NE entries whose conﬁdence scores are less than
0.5 to decrease noisy data.
BERT-T5 As in BERT-NER, we classiﬁed the target papers by BERT into threeclasses of functions (∈ FKC ), namely i) co-occurrence network, ii) correspondence
analysis, and iii) others (see Figure 3 again). We discarded the “others” class,and, for the classes i) and ii), we produced logs given the descriptions by a T5
model. T5 is a language model ﬁrst introduced by Raﬀel et al. [15], with thegreat focus on transfer learning.
4https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking
accessed on 2023/Oct/19.

Table 2. NE tags of co-occurrence network and correspondence analysis.
NE tagMeaning
Freq.
MINAPP Min. # of appearances of a 79
word
MET
Method of calculation of co- 79
occurrence relations
TOP
Display the top N co- 73
occurrence relations
EXT
External variables
41
METNUM Threshold for co-occurrence 34
BOLD
Bolder line for stronger co- 25
occurrence relations
BUBBLE Bubble chart
25
SUBG
Type of community detec- 21
tion
PART
Target parts of speech
13
UNIT
Unit of analysis
10
TYPE
Type of co-occurrence rela- 10
tions
TREE
Display only MST
88
MINDOC Min. document frequency
DIF
Analyze only words with
6
prominent diﬀerences
ORI
Display only word labels far
3from the origin
MAXAPP Max. # of appearances of a
3
word
STAN
Standardize coeﬃcients
3
BUBSIZE Size of a bubble chart
1
MAXDOC Max. document frequency
1
Total
443

Our model was pre-trained on Japanese Wikipedia articles5 , and the pretrained model was transferred to our dataset Texylon.
Hybrid The ﬁnal method of data augmentation, the hybrid dataset, is just the
conjunction of the datasets of swapping and BERT-NER.

5

Evaluations

We evaluated our dataset Texylon and the data augmentation methods for the
log-to-description task and the description-to-log task separately, to ﬁnd out how
our augmentation methods are useful for the tasks.
5

https://huggingface.co/sonoisa/t5-base-japanese accessed on 2023/Oct/19.

5.1

Multi-task Generation Model

As a baseline model for our tasks (§3), we adopted one proposed by Kale etal. [9], which was based on T5 [15]. Here our task was deﬁned as “translation”
between descriptions and logs; hence we regarded the datasets as collections ofcorrect translations from one language (namely “log”) to another (“description”),
and vice versa. We distinguished the two tasks from each other by prepending
preﬁxes like “Translate A to B:,” as it is suggested for a multi-task mixture[15].
To be precise, to each description-log pair (d, ℓ), we associated two data as
follows that represent inputs and outputs for training the model. The ﬁrst data
has as an input “Translate log to text: ℓ” and its output is d. On the other
hand, the second data is a pair of an input “Translate text to log: d” withan output ℓ. We passed to the model all the data which were constructed in that
way.
5.2

Experiment Settings

Unless otherwise speciﬁed below, all the hyperparameters were set to the default
values, or values suggested by inventors/implementers of models.
BERT-NER For the Flair framework, we set an initial learning rate to 0.5,
with a batch size 8. For a BERT model, we used Adam [10] as an optimizer witha learning rate 1e-5 and the maximum number of input tokens 256. We trained
a model for 20 epochs.
BERT-T5 The hyperparameters to BERT are the same as BERT-NER. For a
T5 model, we adopted another optimizer, Adafactor [16], with a learning rate
1e-3, a batch size 8 and the number of steps 100,000.
Multi-task Generation Model We passed the same hyperparameters as
BERT-T5 to a T5 model.
5.3

Cross validation

As the tasks are novel, the datasets are not large enough. Thus we followed the
5-fold cross validation to have more reliable results. First we split our dataset,
Texylon, randomly into ﬁve equal-sized subsets. Then for each fold, we took one
subset for the test and executed the following procedures:
1. form the training-validation subsets from the four subsets other than thetest set,
2. split the training-validation subset into training/validation data with a ratioof 4/1 randomly,

3. augment the training data using the methods explained in §4.2 to have additional data,
4. train the generative model (§5.1) on the concatenation of the training data
and the additional data, and
5. measure the performance of the model on the test subset.
Finally we calculated the averages of evaluation metrics.
5.4

Metrics

Log-to-Description Task We adopted the following metrics, which are widelyused for natural language generation tasks, to calculate to what extent generated
descriptions and reference texts coincide:
1. BLEU-N [13] — weighted geometric mean of pn (1 ⩽ n ⩽ N ) with thebrevity penalty, where pn is the n-gram precision,
2. METEOR [2] — weighted harmonic mean of the uni-gram precision and theuni-gram recall with penalty on adjacent uni-grams,
3. BERTScore [17] — the precision/recall/F1 scores of similarities of token
embeddings.
Description-to-Log Task The metric for the description-to-log task is F1scores, since a log is the well-structured data and it does not need any metrics
for natural language evaluations, contrary to the log-to-description task. Thus wecalculated the F1 scores for two components, namely for functions and for conﬁgurations. In the calculation, if an output is not in the right format (see §4.1),
we ignored it; hence the F1 scores are calculated only among well-formatted logs.
5.5

Results

You can see example outputs for both the tasks in the Tables 3 and 4 respectively.
The results for log-to-description are shown in Table 5. Texylon alone achieved
the highest scores for most of the metrics, and the hybrid was the second best (orhigher) for all the metrics. It implies that the data augmentations may lower theaccuracy in our case. We guess that the main reason is noises in the augmented
data.
Table 6 shows the results for description-to-log. As we see in the table themodel successfully predicted functions with a high accuracy, and there were no
remarkable diﬀerences among the datasets. Moreover, the model produced few
errors for most of the datasets.
We ﬁnd the reasons of drawbacks be in the datasets themselves and in themodels. Because the augmented data can contain a certain amount of noises, it
results in errors for generated texts. The format of logs (§4.1) causes the low
accuracy as well when the language models used for the data augmentation did
not have a plenty of examples in the same format. Therefore, the models canhave failed to recognize them.

Table 3. Examples of description generation from an input log. The rightmost column
represents the English translation. We preserved grammatical or orthographic errors
in English translation as much as possible.
共起ネットワーク (語の最小出 CN(MinApp=25, Top=60)
現数=25, 上位=60)
Texylon
共起ネットワーク分析では，最 For co-occurrence network, we
小出現数を 25 に設定し，描画す set the min. # of appearances
る共起関係の絞り込みにおいて to 25, and set the # of renderは描画数を 60 に設定した．
ings to 60 in the ﬁlter of rendered co-occurrence relations.
Swapping 次に，頻出語の最小出現数を 24 Next, we set the min. # of ap回に設定し，描画する共起関係 pearances of frequent words toの絞り込みにおいては，描画数 24, set the # of renderings toを 60 に設定して，関連が特に強 60 in the ﬁlter of rendered coい語同士を線で結んだ共起ネッ occurrence relations, and creトワーク注 2) を作成した．
ated a co-occurrence network(Notion 2) that links wordsof particularly strong relations
with lines.
図 12012 年版『小学校学習指導 Figure 1 ﬁgure of a coBERT要領解説社会編』における頻出 occurrence network for freNER
語の共起ネットワーク図 (出現 quent words (# of appear回数 25 回以上・共起関係上位 ances ⩾ 25, top 60 co60) (kh coder3 より，筆者作成) occurrence relations) in 2012ver. "Course of Study for Elementary School: Social Studies" (From kh coder3, the author created)
BERT-T5 表 4 は, これらの抽出語のうち, For Table 4, among these ex出現頻度が高い上位 60 語を表 4 tracted words, we show theに, 共起ネットワークを図 1 に top 60 words of highest fre示す.
quency in Table 4, and a cooccurrence network in Figure1.
Hybrid
図-24 は共起ネットワークを表 Figure-24
showsacoす．出現数による単語の取捨選 occurrence
network.
We
択では最小出現数 24 に設定し，set the min. # of appearances
描画する共起関係の絞り込みで to 24 in the word ﬁlter by
は描画数を 60 に設定した．
the # of appearances, andset the # of renderings to
60 in the ﬁlter of rendered
co-occurrence relations.

Input

Table 4. Examples of log generation from an input description. The rightmost column
represents the English translation.
「 失 敗 経 験 」の 内 容 に つ い て We conducted co-occurrence
khcoder(樋口,2014) の共起ネッ network analysis of khcoder
トワーク分析を行なった．解釈 (Higuchi, 2014) about "fail可能性の観点から分析設定は，ひ ure experience." From a point
らがなのみの名詞・動詞・形容 of view of interpretability,
詞・副詞・否定助動詞・形容詞 for analysis conﬁgurations, we(非自立) は削除，出現数は 10 以 deleted hiragana nouns, verbs,
上の語を分析対象とし，jaccard adjectives, adverbs, negative
係数が上位 50 の共起関係を描 auxiliary verbs, adjectives (an画した．
cillary). The target of analysis
is words with the # of appearances ⩾ 10, and we rendered
co-occurrence relations of top
50 Jaccard coeﬃcient.
Texylon
共起ネットワーク (語の最小出現 CN(MinApp=10,
数=10, 描画する共起関係の選択 Select=jaccard coefficient,=jaccard 係数, 上位=50)
Top=50)Swapping 共起ネットワーク (語の最小出現 CN(MinApp=10,Select=jaccard
数=10, 描画する共起関係の選択 coefficient, Top=50)=jaccard 係数, 上位=50)
共起ネットワーク (品詞による CN(SelectByPos=delete with
BERT語の取捨選択=名詞・動詞・形 nouns ... adjectives
NER
容詞・副詞・否定助動詞・形容 (ancillary), MinApp=10,
詞 (非自立) に削除, 語の最小出 Select=jaccard coefficient,
現数=10, 描画する共起関係の選 Top=50)
択=jaccard 係数, 上位=50)
BERT-T5 共起ネットワーク (語の最小出現 CN(MinApp=10,Select=jaccard
数=10, 描画する共起関係の選択 coefficient, Top=50)=jaccard 係数, 上位=50)
Hybrid
共起ネットワーク (品詞による CN(SelectByPos=with nouns
語の取捨選択=名詞・動詞・形 ... adjectives (ancillary),
容詞・副詞・否定助動詞・形容 MinApp=10, Select=jaccard
詞 (非自立) に, 語の最小出現数 coefficient, Top=50)=10, 描画する共起関係の選択=jaccard 係数, 上位=50)

Input

Table 5. Results for the log-to-description task.
Dataset Type BLEU-1 BLEU-2 BLEU-3 BLEU-4 METEOR BERT-P BERT-R BERT-F1
Texylon36.98 24.47 18.20 14.64
30.35
0.73
0.74
0.74
Swapping
34.06
22.24
16.20
12.85
29.39
0.72
0.73
0.73
BERT-NER
33.07
21.33
15.32
11.89
27.81
0.73
0.73
0.73
BERT-T5
24.88
15.70
10.90
8.13
23.94
0.75
0.71
0.72
Hybrid
35.84
23.85
17.43
13.74
29.40
0.74
0.74
0.74
Table 6. Results for the description-to-log task. F1-func for the classiﬁcation of functions, and F1-conf for generating conﬁgurations. The errors are the total numbers of
ill-formatted logs.
Dataset Type F1-func F1-conf Errors
Texylon0.962 0.711
3Swapping
0.960 0.698
4
BERT-NER
0.943 0.717
0BERT-T5
0.947 0.692
12
Hybrid
0.967 0.704
16Conclusion

We introduced Texylon, the dataset of description-log pairs, and proposed fourtypes of data augmentation of that dataset. Then we considered the multi-task
generative model that converts logs to descriptions and vice versa as a baseline model to evaluate the data augmentation methods. Although Texylon itself exhibited the highest scores for log-to-description, the data augmentation
methods equally high scores for description-to-log. The reasons can be that theaugmented data contain noises, or that the baseline model was not suitable forour dataset(s).
Our next plan is to invent a generative model more sensible to the format.
Then, we should employ a new method for data augmentation which makes
better data than Texylon. We would like to build a “general” model as well in asense that it is applicable to other text-anatytics tools than KH Coder.
Disclosure of Interests. The authors have no competing interests to declare thatare relevant to the content of this article.

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