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---
title: 'DYLEN: Diachronic Dynamics of Lexical Networks'
summary: A quick reference guide to the DYLEN tool.
locale: en
authors:
- koenigshofer-elisabeth
- wuensche-katharina
editors:
- zotou-elena
license: cc-by-4-0
locale: en
publicationDate: 2023-02-17
summary: A quick reference guide to the DYLEN tool.
version: 1.0.0
tags:
- data-management
title: "DYLEN: Diachronic Dynamics of Lexical Networks"
license: cc-by-4-0
toc: false
version: 1.0.0
---
## Learning outcomes

After completing this resource, you will

- understand the purpose of DYLEN
- be able to read a visualisation that was created in DYLEN
- know how to undertake an ego network analysis with DYLEN
- generate a general network analysis
* understand the purpose of DYLEN

* be able to read a visualisation that was created in DYLEN

* know how to undertake an ego network analysis with DYLEN

* generate a general network analysis

## Introduction

[DYLEN](https://dylen-tool.acdh.oeaw.ac.at/) is the acronym of the **Diachronic Dynamics of Lexical Networks.** It is an interactive visualisation tool that the Diachronic Dynamics of Lexical Networks project team created to provide insights into the dynamic lexical changes of Austrian German during the 21st century. It helps lexicographers and linguists to analyse the development of Austrian German lexemes over the course of time. It is an open source tool that can be used free of charge.
[DYLEN](https://dylen-tool.acdh.oeaw.ac.at/) is the acronym of the **Diachronic Dynamics of Lexical Networks** (Baumann et al. 2019)**.** It is an interactive visualisation tool (Yim et al. 2022) that the Diachronic Dynamics of Lexical Networks project team created to provide insights into the dynamic lexical changes of Austrian German during the 21st century. It helps lexicographers and linguists to analyse the development of Austrian German lexemes over the course of time. It is an open source tool that can be used free of charge.

[DYLEN](https://dylen-tool.acdh.oeaw.ac.at/) enables lexical network research on large-scale authentic language data that are taken from two Austrian Geman corpora, the [Austria Media Corpus (amc)](https://amc.acdh.oeaw.ac.at/) and [Corpus of Austrian Parliamentary Records (ParlAT)](https://www.oeaw.ac.at/acdh/tools/parlat).
[DYLEN](https://dylen-tool.acdh.oeaw.ac.at/) enables lexical network research on large-scale authentic language data that are taken from two Austrian Geman corpora, the [Austria Media Corpus (amc)](https://amc.acdh.oeaw.ac.at/), (Ransmayr et al. 2017) and [Corpus of Austrian Parliamentary Records (ParlAT)](https://www.oeaw.ac.at/acdh/tools/parlat).

DYLEN provides three options:

- Ego network,
- General network (party),
- General network (speaker),
* Ego network,

* General network (party),

* General network (speaker),

and 2 additional components:

- Node metrics comparison,
- Time series analysis.
* Node metrics comparison,

* Time series analysis.

<Figure src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/picture1.png">
Screenshot of the DYLEN user interface.
Screenshot of the DYLEN user interface.
</Figure>

<Callout kind="note" title="amc and ParlAT">
Both corpora provide large-scale language data on Austrian German from the 21st century.
Both corpora provide large-scale language data on Austrian German from the 21st century.

The amc is one of the largest German language corpora and reflects the Austrian media landscape almost in its entirety (newspapers, magazines, press releases and some news reel transcripts). It contains about 12 billion tokens and is updated every year. It reflects the Austrian media landscape. You can register for access for linguistic analysis [here](https://amc.acdh.oeaw.ac.at/) and learn more about it in this HowTo.
The amc is one of the largest German language corpora and reflects the Austrian media landscape almost in its entirety (newspapers, magazines, press releases and some news reel transcripts). It contains about 12 billion tokens and is updated every year. It reflects the Austrian media landscape. You can register for access for linguistic analysis [here](https://amc.acdh.oeaw.ac.at/) and learn more about it in this HowTo.

The [ParlAT corpus](https://www.oeaw.ac.at/acdh/tools/parlat) comprises the Austrian parliamentary records and contains around 75 million tokens. This corpus is expanded over time, too. It is also part of the CLARIN [ParlaMINT project](https://www.clarin.eu/parlamint).
The [ParlAT corpus](https://www.oeaw.ac.at/acdh/tools/parlat) comprises the Austrian parliamentary records and contains around 75 million tokens (Wissik and Pirker 2018). This corpus is expanded over time, too. It is also part of the CLARIN [ParlaMINT project](https://www.clarin.eu/parlamint).
</Callout>

The following [comic](https://comic.acdh-dev.oeaw.ac.at/howto/comic) provides a visual summary of this article and illustrates the key features of the DYLEN tool.
Expand All @@ -64,29 +70,32 @@ The user interface is very intuitive but every search starts with deciding on ei

Connected words are **semantic neighbours** that share some aspects of the **target word**. Some can even substitute the target word in a particular context. The ego network visualises the **50** most closely related semantic neighbours of a target word. Note that it does not show the target word itself because it would render the visualisation impossible to read. The semantic neighbours are classified as parts of speech (POS), e.g. noun, proper nouns and verbs.

<Figure alt="A graph, two line graphs to show the semantic neighbours, node metrics and time series analysis for the word 'Geld' in the amc texts in 1996." src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/egonetwork-interface.png">
Ego network of the word "Geld" (money), taken from the amc texts in 1996.
<Figure src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/egonetwork-interface.png" alt="A graph, two line graphs to show the semantic neighbours, node metrics and time series analysis for the word 'Geld' in the amc texts in 1996.">
Ego network of the word "Geld" (money), taken from the amc texts in 1996.
</Figure>

#### Instructions:

<Figure src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/picture2.png">
Input field for ego network
Input field for ego network
</Figure>

On the input field on the left side bar, you can

1. select a corpus (i.e., amc or PARLAT),

2. select a subcorpus (e.g., a specific newspaper),

3. type a target word (e.g., 'Geld'),
4. and finally click _Visualise_.

4. and finally click *Visualise*.

#### Understanding the visualisation

Once you clicked on visualise, DYLEN will generate your network. Let us stick with our "Geld" (money) example.

<Figure alt="The ego network for 'Geld': differently sized nodes connected by lines, a timeline on the top and parts of speech in different colours" src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/egonetwork_geld_nodedetail.png">
The ego network for "Geld" (money)
<Figure src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/egonetwork_geld_nodedetail.png" alt="The ego network for 'Geld': differently sized nodes connected by lines, a timeline on the top and parts of speech in different colours">
The ego network for "Geld" (money)
</Figure>

Above, you see the semantic neighbours represented by nodes that can be dragged further apart to get a better overview. Their size indicates their frequency. The bigger the node, the more commonly it is used in the corpus. You can click on each node to highlight the connections. The colours represent different parts of speech and you can change them to your preference.
Expand All @@ -97,38 +106,38 @@ The Time Series Analysis allows to compare two words over time; the comparison c

#### Metrics and Node metric comparison

<Figure alt="A bar with five sliders" src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/parallelcoordinatesoptions.png">
Parallel coordinates options: normalised frequency, degree centrality, betweenness centrality, pagerank, clustering coefficient.
<Figure src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/parallelcoordinatesoptions.png" alt="A bar with five sliders">
Parallel coordinates options: normalised frequency, degree centrality, betweenness centrality, pagerank, clustering coefficient.
</Figure>

In addition, you can select the metrics for the parallel coordinates with the sliders. These metrics are presented in the parallel coordinates plot. Every graph line represents one word and each vertical axis stands for the value in the respective metric. You can visualise all words or selected words in the node metrics comparison. When you click the lines, you can inspect the values for each metric.

<Figure alt="Four lines cut by five axes for the words: verurteilen, Strafe, Haft and Gefängnis" src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/node-metric-comparison_geldstrafe.png">
Lines and fives axes for the words "verurteilen", "Strafe", "Haft", "Gefängnis" when analysing an ego network for "Geldstrafe" in 1996.
<Figure src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/node-metric-comparison_geldstrafe.png" alt="Four lines cut by five axes for the words: verurteilen, Strafe, Haft and Gefängnis">
Lines and fives axes for the words "verurteilen", "Strafe", "Haft", "Gefängnis" when analysing an ego network for "Geldstrafe" in 1996.
</Figure>

<Callout kind="note" title="What do the metrics mean?">
#### Frequency
#### Frequency

...represents how often a word occurs.
...represents how often a word occurs.

#### Degree centrality
#### Degree centrality

...represents how connected a word is. It shows the total number of edges linked to a node. In the example, "Haft" has a higher degree centrality than "Strafe", meaning that it is more strongly connected.
...represents how connected a word is. It shows the total number of edges linked to a node. In the example, "Haft" has a higher degree centrality than "Strafe", meaning that it is more strongly connected.

#### Betweenness centrality
#### Betweenness centrality

...represents the number of shortest paths that pass through that node. It shows how frequently nodes tand between each other. Again, "Haft" shows more betweenness centrality than "Strafe", meaning that more shorter paths pass through "Haft".
...represents the number of shortest paths that pass through that node. It shows how frequently nodes tand between each other. Again, "Haft" shows more betweenness centrality than "Strafe", meaning that more shorter paths pass through "Haft".

#### Pagerank
#### Pagerank

...represents the notion that a node is as important as the combined importance of its linked nodes.
...represents the notion that a node is as important as the combined importance of its linked nodes.

#### Clustering coefficient
#### Clustering coefficient

...measures the degree to which nodes in a graph tend to cluster together.
...measures the degree to which nodes in a graph tend to cluster together.

_The above explanations are taken from the_ DYLEN _tool website and can be accessed and read in more detail via the information button in the node metric analysis visualisation._
*The above explanations are taken from the* DYLEN *tool website and can be accessed and read in more detail via the information button in the node metric analysis visualisation.*
</Callout>

### General networks
Expand All @@ -140,27 +149,32 @@ General networks reflect the speeches of a particular politician or a political
On the input field on the left side bar, you can

1. select a party,

2. select a speaker (only for general network (speaker)),
3. (optional, but recommended) use the _Node filter_ to
- select a metric (e.g. degree centrality)
- adjust the percentage of nodes to be displayed,
4. and finally click _Visualise_.

<Figure alt="Four visualisations for general network (party)" src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/generalnetwork_party_comparison_spö-övp2000.png">
General network (party) comparison for SPÖ and ÖVP in 2000. The word "brauchen" (need) and its connections are highlighted in the first network visualisation.
3. (optional, but recommended) use the *Node filter* to

* select a metric (e.g. degree centrality)

* adjust the percentage of nodes to be displayed,

4. and finally click *Visualise*.

<Figure src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/generalnetwork_party_comparison_spö-övp2000.png" alt="Four visualisations for general network (party)">
General network (party) comparison for SPÖ and ÖVP in 2000. The word "brauchen" (need) and its connections are highlighted in the first network visualisation.
</Figure>

### Node Metrics Comparison

The general network analysis allows for node metric comparison too. You can choose between the same metrics as in the ego network. When you compare two parties or speakers, each component gets a different colour. Also, you can ask DYLEN to return a table for the node metrics with indicating colours (see below).

<Figure alt="Table with metric columns showing words in alphabetical order and the metric values." src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/generalnetwork_party_nodemetrics_spö_övp_2000.png">
In the table, one can see a table of nodes and the values for the respective metrics.
<Figure src="/assets/content/resources/introduction-to-diachronic-dynamics-of-lexical-networks/generalnetwork_party_nodemetrics_spö_övp_2000.png" alt="Table with metric columns showing words in alphabetical order and the metric values.">
In the table, one can see a table of nodes and the values for the respective metrics.
</Figure>

### Time Series Analysis

In the general network analysis, the development of speakers or parties can be traced, like the ego network traces individual words. You can visualise your results as a graph on a timeline, or as a table with selected metrics and values. All your options for analysis are explained in more detail on the [DYLEN website](https://dylen-tool.acdh.oeaw.ac.at/) in the technical details in the _Time Series Analysis_ tab.
In the general network analysis, the development of speakers or parties can be traced, like the ego network traces individual words. You can visualise your results as a graph on a timeline, or as a table with selected metrics and values. All your options for analysis are explained in more detail on the [DYLEN website](https://dylen-tool.acdh.oeaw.ac.at/) in the technical details in the *Time Series Analysis* tab.

## Links

Expand All @@ -171,3 +185,13 @@ In the general network analysis, the development of speakers or parties can be t
[DYLEN Comic](https://comic.acdh-dev.oeaw.ac.at/howto/comic)

[HowTo use the amc and CQL](/resources/corpus-query-language-im-austrian-media-corpus)

## References

* Baumann, Andreas, Julia Neidhardt, and Tanja Wissik. 2019. DYLEN: [Diachronic Dynamics of Lexical Networks](https://ceur-ws.org/Vol-2402/#paper5). In *Proceedings of the Poster Session of the 2nd Conference on Language, Data and Knowledge (LDK-PS 2019)*, ed. Thierry Declerck and John P. McCrae, 2402:24–28. CEUR Workshop Proceedings. Leipzig, Germany: CEUR.

* Ransmayr, Jutta, Karlheinz Mörth and Matej Ďurčo (2017): AMC (Austrian Media Corpus) – Korpusbasierte Forschungen zum österreichischen Deutsch. In Digitale Methoden der Korpusforschung in Österreich (= Veröffentlichungen zur Linguistik und Kommunikationsforschung Nr. 30), Hrsg. C. Resch und W. U. Dressler, 27-38. Wien: Verlag der Österreichischen Akademie der Wissenschaften.

* Wissik, Tanja, and Hannes Pirker. 2018. [ParlAT beta Corpus of Austrian Parliamentary Records](https://www.oeaw.ac.at/acdh/tools/parlat). In *LREC2018 Workshop ParlaCLARIN: Creating and Using Parliamentary Corpora In Proceedings of the Eleventh International Conference on Language Resources and Evaluation LREC2018*, ed. Darja Fišer, Maria Eskevich, and Franciska de Jong. Miyazaki: European Language Resources Association.

* Yim, Seung-bin, Katharina Wünsche, Asil Cetin, Julia Neidhardt, Andreas Baumann, and Tanja Wissik. 2022. [Visualizing Parliamentary Speeches as Networks: the DYLEN Tool](https://aclanthology.org/2022.parlaclarin-1.9). In *Proceedings of the Workshop ParlaCLARIN III within the 13th Language Resources and Evaluation Conference*, ed. Darja Fišer, Maria Eskevich, Jakob Lenardič, and Franciska de Jong, 56–60. Marseille, France: European Language Resources Association.

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