DYLEN: Diachronic Dynamics of Lexical Networks

koenigshofer-elisabeth; wuensche-katharina

DYLEN: Diachronic Dynamics of Lexical Networks

Written by: Elisabeth Königshofer and Katharina Wünsche
Published on: February 17, 2023
Tagged with: Data management

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

Introduction

DYLEN 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 enables lexical network research on large-scale authentic language data that are taken from two Austrian Geman corpora, the Austria Media Corpus (amc), (Ransmayr et al. 2017) and Corpus of Austrian Parliamentary Records (ParlAT).

DYLEN provides three options:

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

and 2 additional components:

Node metrics comparison,
Time series analysis.

The following comic provides a visual summary of this article and illustrates the key features of the DYLEN tool.

Networks

Diachronic networks derive from the texts in amc and ParlAT with the help of word embeddings. In NLP, word embeddings are representations of words.

The user interface is very intuitive but every search starts with deciding on either an ego network or a general network (party or speaker). In each network type, you can analyse various parameters of a single entity or compare two entities. The first step on your diachronic network journey is to select the network that you would like to generate.

Ego network

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.

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.

Instructions:

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

select a corpus (i.e., amc or PARLAT),
select a subcorpus (e.g., a specific newspaper),
type a target word (e.g., ‘Geld’),
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.

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)

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.

Time Series Analysis

The Time Series Analysis allows to compare two words over time; the comparison can be relative to the first year, last year or previous year.

Metrics and Node metric comparison

A bar with five sliders — Parallel coordinates options: normalised frequency, degree centrality, betweenness centrality, pagerank, clustering coefficient.

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.

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.

What do the metrics mean?

Frequency

…represents how often a word occurs.

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.

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”.

Pagerank

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

Clustering coefficient

…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.

General networks

General networks reflect the speeches of a particular politician or a political party. Those networks are larger than ego networks and require more filters that make the visualisation more legible. Under general networks you can explore frequent lexemes used by particular political parties (general network (party)) or individual politicians (general network (speaker)) in the Austrian Parliament.

Instructions:

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

select a party,
select a speaker (only for general network (speaker)),
(optional, but recommended) use the Node filter to
- select a metric (e.g. degree centrality)
- adjust the percentage of nodes to be displayed,
and finally click Visualise.

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.

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).

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.

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 in the technical details in the Time Series Analysis tab.

Links

About the DYLEN Project

DYLEN Tool

DYLEN Comic

HowTo use the amc and CQL

References

Baumann, Andreas, Julia Neidhardt, and Tanja Wissik. 2019. DYLEN: Diachronic Dynamics of Lexical Networks. 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. 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. 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.