Measuring and Visualizing Domain Specific Word Use

(Work in progress)

Goals and approach

Language adapts to the communicative needs of its context, along dimensions such as domain, register, and time. This is reflected in preferential choices of vocabulary, grammar, meaning, and style, which differentiate language use by context. This visualization aims at exploring domain specific word use based on three related but complementary measures:

Productivity and Ambiguity require a representation of word use in a domain. To this end, we use word embeddings (Mikolov et al. 2013), which have become a popular means to analyze the semantics of words based on their actual use in large corpora. The main idea of word embeddings is to reduce the very high dimensionality of word co-occurrence contexts, the size of the vocabulary, to few dimensions, such as 100-200. For the word embeddings used in this visualization, we employ the structured skip gram approach by Ling et al. (2015), using a one-hot encoding for words as input layer, a 200 dimensional hidden layer, and a positional one-hot encoding for the context words in a window of [-5,5] as the output layer. This approach takes word order into account, and thus also captures syntactic regularities of word use.

As base corpus, we use the DeReKo-2017-II edition of the German Reference Corpus (Institut für Deutsche Sprache 2017; Kupietz et al 2010, 2018) containing 33 billion tokens. For the separation into domains, we use 11 of the top-level domain categories annotated in the metadata of DeReKo texts (Weiss 2005) and Wikipedia discussions (ref). The individual domains contain between 80 million and 9 billion tokens (see Figure 1).

Domain Distribution
Figure 1: Distribution of Domains

For deriving domain specific word embeddings we adapt the approach of Dubossarsky et al. (2015), Fankhauser and Kupietz (2017), there used for diachronic corpora (see also Diachronic Visualization). We first compute embeddings for the base corpus and use them to initialize training of domain specific embeddings. Thereby the embeddings are comparable between individual domains and the base corpus. On this basis the paradigmatic neighbourhood of words with similar meanings is defined by the cosine similarity of their embeddings. To visualize the embeddings we further reduce their 200 dimension to two dimensions using t-distributed stochastic neighbourhood embedding (Van der Maaten & Hinton 2008).

A quick guide

The visualization consists of three main areas:

To the left, a bubble chart represents the color encoded semantic space of words, with the size of bubbles proportional to the square root of the relative frequency in the chosen domain, and the color indicating the domain of a word. Words with similar use typically are positioned closely to each other.

The top right provides a heatmap representing the overall distance between the individual domains, formalized as the Kullback Leibler Divergence of their unigram language models, ranging from deep red for a large distance (Fiction, Sports, Wikipedia Discussions) to yellow for a small distance. The heatmap doubles as a selector for choosing a pair of domains to compare with each other, the main diagonal serves for comparing a domain with the base corpus. Clicking on the title DeReKo Domains chooses the (initial) overall comparison of the most typical words for each domain with the base corpus.

The top right also provides a number of visualization options.

The bottom right provides a comparison word list for the chosen pair of domains, with the following columns:

The word list can be sorted on all columns, searched for words, and filtered by giving lower bounds. Filtering is in particular useful to focus on words with a minimum frequency per million, or on words typical for a domain (kld > 0). Clicking on a word zooms and centers the bubble chart on its position in the first domain, a double click selects its position in the second domain. This allows to quickly inspect its different meanings by its paradigmatic neighbours.

Available visualizations

The following visualizations are currently available:

The tsne hyperparameter Perplexity determines roughly how well local neighbourhoods are preserved in two dimensions. Perplexities 100 and 200 seem to work better than 30. Note that the visualization takes some time to load (depending on your connection between 10 and 60 seconds).

Some example analysis

Macro Analysis

To illustrate the introduced measures and their interaction, we start with a macro analytic perspective:

Figure 2(a) shows how specific word use is in the indivual domains by means of the Jensen-Shannon divergence between their (unigram) language models. Red means high divergence, yellow means low. We can see that fiction (FI), sports (SP), and wikipedia discussions (WD) stand out with rather specific word distributions, and thus (reddish) high divergence, whereas, e.g., politics (PO) and society (SG) are rather close.

FI 0.00 0.32 0.32 0.23 0.29 0.29 0.36 0.21 0.36 0.39 0.30 0.33
FR 0.32 0.00 0.16 0.11 0.16 0.13 0.21 0.11 0.14 0.20 0.14 0.36
GE 0.32 0.16 0.00 0.20 0.16 0.13 0.27 0.11 0.15 0.16 0.09 0.31
KU 0.23 0.11 0.20 0.00 0.21 0.15 0.22 0.12 0.21 0.23 0.16 0.30
NU 0.29 0.16 0.16 0.21 0.00 0.19 0.27 0.15 0.18 0.25 0.15 0.36
PO 0.29 0.13 0.13 0.15 0.19 0.00 0.23 0.08 0.13 0.11 0.12 0.27
SP 0.36 0.21 0.27 0.22 0.27 0.23 0.00 0.21 0.25 0.26 0.26 0.37
SG 0.21 0.11 0.11 0.12 0.15 0.08 0.21 0.00 0.14 0.16 0.13 0.22
TI 0.36 0.14 0.15 0.21 0.18 0.13 0.25 0.14 0.00 0.16 0.13 0.36
WF 0.39 0.20 0.16 0.23 0.25 0.11 0.26 0.16 0.16 0.00 0.15 0.31
WI 0.30 0.14 0.09 0.16 0.15 0.12 0.26 0.13 0.13 0.15 0.00 0.28
WD 0.33 0.36 0.31 0.30 0.36 0.27 0.37 0.22 0.36 0.31 0.28 0.00
(a) Jensen-Shannon divergence
FI 92.0 0.7 0.2 2.0 0.3 0.6 0.2 1.6 0.1 0.6 0.5 1.0
FR 0.1 87.1 0.6 3.7 0.4 1.7 1.7 1.3 1.5 0.8 1.0 0.1
GE 0.1 2.0 83.6 0.4 1.0 3.0 0.2 2.1 1.3 3.1 2.4 0.9
KU 0.5 4.9 0.1 89.5 0.1 1.1 1.0 1.2 0.1 0.4 0.6 0.6
NU 0.5 2.2 1.8 0.2 89.7 0.4 0.6 0.6 1.6 0.3 1.9 0.1
PO 0.1 1.8 1.0 1.0 0.1 87.1 0.5 1.8 0.7 4.3 0.7 1.0
SP 0.0 1.2 0.1 0.5 0.0 0.5 96.9 0.2 0.1 0.3 0.0 0.1
SG 0.9 2.8 1.9 3.4 0.4 3.7 0.7 81.9 1.8 1.3 0.1 1.3
TI 0.0 1.9 0.9 0.2 0.4 1.8 0.4 1.3 88.4 2.4 1.6 0.6
WF 0.1 0.6 1.3 0.6 0.0 5.4 0.6 0.9 1.6 85.1 2.3 1.5
WI 0.4 1.4 1.9 1.1 1.1 1.1 0.0 0.2 2.5 2.7 86.5 1.2
WD 0.6 0.4 0.6 1.4 0.0 2.2 0.2 1.0 0.8 2.6 1.5 88.8
(b) Confusion matrix for domains of nearest neighbours
Figure 2: Overall comparison of domains.

Figure 2(b) illustrates that paradigmatically related words are often typical for the same domain. Each cell at position Domain y/Domain x gives the probability (*100) that the nearest neighbour of a word typical for Domain y (with maximum cosine distance 0.3) is typical for Domain x. Overall, there is a 89% chance that the nearest neighbour is typical for the same domain (main diagonal). For fiction and sports the chance is even 92% and 97%, their typical words thus form the most tightly knit paradigmatic clusters.

Perplexity 30
(a) Perplexity 30
Perplexity 100
(b) Perplexity 100
Perplexity 200
(c) Perplexity 200
Figure 3: 2D distribution of most typical words for wikipedia discussions (red) vs. DeReKo (yellow)

Figure 3 provides a more indepth perspective on domain specific paradigmatic clusters by visually correlating domain (color) with semantics (position in 2D) of words. Especially for perplexities 100 and 200, which preserve local neighbourhoods fairly well, we can see that words typical for wikipedia discussions (red) populate rather specific regions, whereas words in the base corpus DeReKo (yellow) populate the entire semantic space [4].

Perplexity 30
(a) Perplexity 30
Perplexity 100
(b) Perplexity 100
Perplexity 200
(c) Perplexity 200
Figure 4: 2D distribution of most typical words for all domains

Figure 4 shows the 2D distribution of the most typical words for all domains at various perplexities. Again, especially for higher perplexities, we can clearly identify clusters of paradigmatically related words typical for the same domain.

kld distribution
Figure 5: KLD distribution

Finally, Figures 5-7 show the distributions of the individual measures for selected domains. Figure 5 shows the distributions of individual KLD values (typicality) > 0.001, words with smaller and negative KLD values constitute the majority, but do not contribute to typicality. Mean (blue) and median (red) of the domains with distinctive language models fiction, sport, wikipedia discussions are significantly larger than for domains closer to the base corpus, such as politics.

entropy distribution
Figure 6: Entropy distribution

Figure 6 shows the distributions of the individual entropies (paradigmatic productivity). These show an opposite trend. On average, the domains fiction and wikipedia discussions have lower paradigmatic productivity (mean and even more strikingly median). One reason for this may be the technical setup: We currently only consider the most frequent 30.000 words in the base corpus DeReKo, and the language models of these two domains are rather distinct. Thus, some domain specific pardigmatic neighbours may not be included in the overall top 30.000, causing artificially low entropies for some domain specific words which are in the top 30.000. However, sports has a rather distinct language model too, but displays rather high productivity. This requires further careful analysis. Figure 7 shows means and medians of the paradigmatic productivities for all domains. In addition, the distributions are not as smooth as the distributions for kld and distance, but contain gaps, especially on the low end. This is most likely due to the somewhat arbitrary cut-off at maximum cosine-distance 0.3, which causes many words to have no neighbours, and thus entropy 0. However, on the higher end, the distributions are fairly smooth.

entropy means per domain
Figure 7(a): Mean productivity per domain
entropy medians per domain
Figure 7(b): Median productivity per domain

Figure 8 shows the distributions of the cosine-distances (ambiguity). On average, these show a similar trend as the kld distributions, i.e., domains with specific unigram language models (kld), e.g., fiction and wikipedia discussions also have a higher mean and median distance. We can see that the domain specific training regime leads to a small but general meaning shift, i.e., there are rather few words which do not change their meaning at all.

distance distribution
Figure 8: Distance distribution

Micro Analysis

Table 1 displays the top 10 words by kld (typicality) for selected domains in comparison to the base corpus. Just 10 words can of course not sufficiently characterize a domain, but still the largerly interpersonal discourse in fiction and wikipedia discussions is hinted at by personal pronouns, the nominal style of politics and science by definite articles and passive (werden), and the main themes of culture and sports by according thematic words.

fiction culture politics sports science discussions
ich </s> </s> <num> </s> <num>
daß und die </s> werden du
</s> er der gegen die ich
er Musik dass den sind Artikel
sie von für Trainer oder nicht
du mit werden Spiel Forscher </s>
und als nicht Mannschaft ist Du
mir Film sei Sieg von Ich
mich Publikum des Saison Wissenschaftler ist
so Band Stadt Platz etwa Wikipedia
Table 1: Top 10 words by kld for selected domains

Table 2 gives the top 10 words by their domain specific paradigmatic productivity, discarding non typical words with negative kld. fiction contains an assortment of past tense and modal words, some adverbial connectors, and some numerals, which, like named entities are of course inherently productive. wikipedia discussions lists a larger variety of adverbial connectors together with adjectives expressing criticism. culture contains mainly judgemental adjectives with clearly positive sentiment, whereas politics leans towards judgemental adjectives with predominantly negative sentiment. sports also contains some adjectives with positive sentiment, together with other entries with rather low kld with less obvious relationship to sports. In science the inherently productive field of color adjectives appears on top. As mentioned above, the paradigmatic neighbourhoods of named entities are typically very productive, and are thus excluded in Table 2.

fiction culture politics sports science discussions
Gleichwohl mitreißenden absurd enzyklopädisch rosa fragwürdig
Überdies humorvollen inakzeptabel sensationellen orange Nichtsdestotrotz
sollte stimmungsvollen kontraproduktiv bemerkenswerten Energie- Jedenfalls
würde eindrucksvollen abwegig Formulierung Bohnen problematisch
zog fantastischen ärgerlich eindrucksvollen gelben Allerdings
dreißig originellen Verkehrs- Verlinkung weiße Folglich
fünfzehn witzigen unverständlich Revert Arbeits- Andererseits
legte grandiosen Sozial- grandiosen Kartoffeln verwirrend
schlug faszinierenden akzeptabel überraschenden Zwiebeln Deswegen
nahm großartigen Umwelt- Machern blaue Natürlich
Table 2: Top 10 words by productivity for selected domains

Finally, Table 3 gives examples for words with domain specific meaning in comparison to their dominant meaning in the base corpus. Apart from the frequent case of a noun doubling as a named entity (such as last name or organization), we distinguish three main cases of ambiguity: Differences in part-of-speech (and possibly meaning), differences in meaning but identical part-of-speech, and differences in meaning which hint at (half dead) metaphors or otherwise figurative speech. The last case seems to be particularly popular in sports.

Different Part-of-Speech
Different Meaning
word dom difference word dom difference word dom difference
eignen FI adj. vs. verb Steuer FI taxes vs. steering Schere FI difference (met.) vs. scissors
weise FI adj. vs. verb versetzte FI replied vs. moved Blütezeit NU season vs. era (met.)
begriff FI verb vs. noun Novelle KU novella vs. amendment Kasten SP goal (met.) vs. box
Au FI exclamation vs. location Besprechung KU review vs. meeting Gehäuse SP goal (met.) vs. container
Teils NU adv. vs. noun Vorstellung KU performance vs. idea Maschen SP (goal) mesh (met.) vs. stitch
verdienten SP adj. vs. verb Anhang SP followers vs. appendix Packung SP high loss (met.) vs. packing
Bekannte SP noun vs. adj. Puppen WI pupas vs. dolls Weichen TI points vs. course (met.)
Table 3: Examples of multiple meanings


  1. Typicality is defined as its contribution to the Kullback-Leibler Divergence between the (unigram) language model P of a domain to the language model Q of another domain: P(w)*log(P(w)/Q(w)), also called relative entropy. This gives the number of bits lost when encoding w with an optimal encoding for Q instead of P. (see also: Contrastive Analysis)
  2. Productivty is measured by the Entropy H(P)=-Sum_i*P(w_i)*log(P(w_i)), where P(w_i) = cos(w,w_i)*freq(w_i|w)/(Sum_k cos(w,w_k)*freq(w_k|w)) is the (conditional) probability of word w_i in the close neighbourhood of word w, weighted by the cosine similarity cos between w_i and w (max 25 words, cosine distance < 0.3). For the chosen parameters this measure ranges between 0, no neighbours and log(25) = 4.64, all 25 neighbours with maximum similartiy 1, uniformly distributed. Note that the term productivity is borrowed from analysis of word formation. Pexman et al. (2008) employ a closely related measure - number of paradigmatic neighbours - as an aspect of semantic richness.
  3. Other measures for ambiguity exist, such as curvature aka clustering coefficient (Dorow et al. 2005). Moreover, there exist more sophisticated approaches to analyse and detect ambiguity via word embeddings, for an overview see e.g. Van Landeghem (2016).
  4. The bubble distributions are produced with the following settings: Zoom by: kld, Domain: both, Show: bubbles (only).


Peter Fankhauser. fankhauser at