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The Frankfurt Latin Lexicon: From Morphological Expansion and Word Embeddings to SemioGraphs.

In this article we present the Frankfurt Latin Lexicon (FLL), a lexical resource for Medieval Latin that is used both for the lemmatization of Latin texts and for the post-editing of lemmatizations. We describe recent advances in the development of lemmatizers and test them against the Capitularies corpus (comprising Frankish royal edicts, mid-6th to mid-9th century), a corpus created as a reference for processing Medieval Latin. We also consider the post-correction of lemmatizations using a limited crowdsourcing process aimed at continuous review and updating of the FLL. Starting from the texts resulting from this lemmatization process, we describe the extension of the FLL by means of word embeddings, whose interactive traversing by means of SemioGraphs completes the digital enhanced hermeneutic circle. In this way, the article argues for a more comprehensive understanding of lemmatization, encompassing classical machine learning as well as intellectual post-corrections and, in particular, human computation in the form of interpretation processes based on graph representations of the underlying lexical resources.


Introduction
Regarding lexical resources for Natural Language Processing (NLP) of historical languages such as Latin, three paradigms can be roughly distinguished: (1) morphologically enriched lexica or dictionaries such as the Frankfurt Latin Lexicon (FLL) to be presented here, which use, for example, rules of morphological expansion to generate inflected forms from lemmas collected from web and other resources, (2) wordnets such as the famous WordNet (Miller, 1995), which as a terminological ontology (Sowa, 2000) distinguishes (wordforms as search terms of) lemmata from synsets and their sense relations, and (3) word embeddings (Komninos and Manandhar, 2016;Levy and Goldberg, 2014;Ling et al., 2015; which address the statistical modeling of syntagmatic (contiguity) and paradigmatic (similarity) associations of lexical units (Halliday and Hasan, 1976;Jakobson, 1971;Miller and Charles, 1991;Raible, 1981). 1 Ideally, for a historical language such as Latin, there exists an integrated system of resources of these kinds in a sufficiently deep state of development: by using such a resource, a professional user or NLP system is extensively informed about the lexical units of the target language on different levels of lexical resolution (including wordforms, lemmata, superlemmata, lexeme groups etc.), about their morpho-syntactic and semantic features as well as about their various sense relations and unsystematic associations. Regarding the example of lemmatization, such a resource would support both the initial automatic lemmatization and its intellectual postcorrection, which in turn would be the starting point for the post-correction or further development of this resource, thereby closing the digitally enhanced hermeneutic circle. However, the example of the Latin WordNet (Minozzi 2017) already shows that the components of such an integrated resource are still out of reach for this historical language (as explained and analyzed in Franzini et al., 2019). The same applies to input-intensive word embeddings, which require large amounts of text data, but which are not yet freely available for Latin (see, however, UDify (Kondratyuk and Straka, 2019) as an example of an approach that seems to circumvent this limitation -cf. Section 4). Last but not least, voluntarily created lexical information systems such as Wiktionary, which aim to integrate wordnet-related information with dictionary information, suffer not only from a lack of scope, but also from multiple sources of information biases (cf. Mehler et al. 2017). Therefore, it remains a challenge to provide not only one of the three types of resources (dictionary, wordnet, embeddings) for Latin in sufficient quantity, but even more to do so for at least two of these types -in an integrated manner. This article wants to take a step in this direction. That is, we present the FLL as a kind of Latin dictionary that distinguishes lexical units at the level of word forms, syntactic words 2 , lemmata, and superlemmata, provides rich grammatical information for syntactic words, serves as a resource for the post-correction of automatic lemmatization, provides a word-for-word monitoring of the lemmatization status of each text, which is particularly easy to read for non-IT philologists, and supports the computation of word embeddings at various levels of lexical resolution. We also show how these embeddings can be presented as interactive graphs to encourage the correction and further development of the underlying resources.
The article is organized as follows: Section 2 outlines the structure of the FLL and quantifies the extent of its overgeneration or, conversely, its lack of coverage. Section 3 deals with the post-processing of the lemmatization of Latin texts with the help of the FLL, while Section 4 compares the current progress of lemmatizers for Latin. Subsequently, Section 5 deals with the derivation of genre-sensitive word embeddings for Latin and their visualization by means of interactive SemioGraphs. These visualizations are then used in Section 6 to conduct three case studies in computational historical semantics, which ultimately combine lemmatization and the evaluation of word embedding graphs with the underlying FLL. In this way, we will speak of a digitally enhanced hermeneutic circle implemented through the NLP pipeline for Latin, as presented in this article. Finally, Section 7 draws conclusions and gives an outlook on future work.

From Superlemmata to Syntactic Words
The Frankfurt Latin Lexicon 3 (FLL) is a morphological lexicon, currently for Medieval Latin, that is Latin between 400 and 1500 CE. Its main purpose is to support the automatic lemmatization of Latin texts with the Text-technology Lab Latin Tagger (TTLab Tagger) (Gleim et al., 2019;cf. Stoeckel 2020), which is available through the TextImager 4 (Hemati et al., 2016), the eHumanities Desktop 5 (Gleim et al., 2012) and GitHub 6 . It was created starting in 2009 (cf. Mehler et al., 2011;see also Jussen et al. 2007) by extracting and collecting lemmata from various web-based resources. This in-cludes 7 the AGFL Grammar Work Lab 8 (Koster and Verbruggen, 2002), the Latin morphological analyzer LemLat (Passarotti, 2004), the Perseus Digital Library (Crane, 1996), William Whitaker's Words 9 , the Index Thomisticus 10 (Passarotti and Dell'Orletta, 2010), Ramminger's Neulateinische Wortliste 11 , the Latin Wiktionary 12 , Latin training data of the Tree Tagger (Schmid, 1994), the socalled Najock Thesaurus 13 , and other resources from cooperating projects like "Nomen et Gens" that provide several thousands of Latin personal names 14 . Since then, the FLL has grown continuously through the lemmatization of Latin Texts. 15 The entries of the FLL are structured according to a four-level model consisting of wordforms, syntactic words (mapping wordforms onto vectors of grammatical features), lemmata and superlemmata. The introduction of the superlemma level was particularly important for preserving the considerable orthographical richness of Medieval Latin as a historical language. This approach is analogous to the Wiktionary model of lexical units, but in contrast to Wiktionaries and above all wordnets (such as the Latin WordNet -cf. Franzini et al. 2019) it lacks lexical-semantic relations (see Section 1).

PoS
The superlemma provides the normalized spelling of a lemma so that on the lemma level different spellings can be kept. The FLL currently contains 116,297 superlemmata and 133,691 subordinated lemmata. These lemmata have been expanded morphologically according to the standard grammar of Classical Latin as described in Menge (2009) and Rubenbauer et al. (2009) so that the FLL now has 9,663,808 syntactic words (see Table 1 for earlier statistics of the FLL). 16 In the future, the Superlemma-ID will be used to connect the FLL with other lexical resources on the web (and also with resources provided by traditional long-term institutions for Latin lexicography), so that morphological information will be available together with reading aids. The lexicon could also work with a fourth (lexeme group) and a fifth level (synset) to bundle superlemmata of different POS that share the same root, or to map semantic relations. But this is future work.
The FLL can map multi-word units, which makes it easier to record proper names such as Colonia Agrippina. However, the four-step model currently meets the requirements of lemmatization. The lexicon is managed via the Lexicon Browser of the eHumanities Desktop, which has been especially adapted for humanities scholars without programming skills. Entries can be created, changed, merged, reorganized or deleted by authorized users. The so-called Extension Tool then creates all inflected forms for newly entered lemmata. Only basic information is needed to identify the right declination or conjugation for the new token. All changes are documented by naming the authors and timestamps. Additional columns allow descriptions to be added to the entries or to show if an entry has been double-checked. This procedure was developed together with various third-party funded humanities projects that have based their philological and linguistic research on their work with the FFL. 17 16 Apart from some exceptions like the oblique case, the grammar rules did not alter between Classical and Medieval Latin; cf. (Menge, 2009;Rubenbauer et al., 2009 An objection to the method of morphological expansion, and thus to lexica of the type of the FLL, is to say that it is prone to overgeneration. To calculate this, we used as a reference corpus a repository of Latin texts including Migne Patrologia Latina (MPL), substantial parts of the Monumenta Germaniae Historica (MGH) and other repositories 18 to ask for the number of syntactic words of the FLL that our lemmatization finds manifested in this repository. This is shown in Table 2. Indeed, only 9% of the syntactic words of the FLL are found in this "text repository". Figure 1 shows how this coverage grows with the percentage of tokens of the reference corpus covered by the FLL. It demonstrates almost a linear trend -except for the rightmost part of the distribution: the number of syntactic words in the FLL that are observed in the corpus grows linearly with the size of this corpus. The coverages are much higher on the level of lemmata (77.09%) and superlemmata (76.47%). On the other hand, the underrepresentation, that is, the number of tokens within our reference corpus that are unknown from the point of view of the FLL, is remarkably low (2.84%). Such a rate of coverage did not seem to be possible in the early days of the FLL: in fact, it was the morphological expansion that made it an extensive lexicon that allows such rates, so that with each lemmatized token the associated grammatical features can be linked. Furthermore, the corpus of Medieval Latin texts within the eHumanities Desktop 19 is continuously extended, with each syntactic word being identified by a corresponding corpus frequency. These frequencies allow the subsequent filtering of supposedly overgenerated words for downstream tasks or even for information retrieval by users.

Crowdsourcing the FLL
The FLL grows as new texts are uploaded into the text database of HSCM 20 and lemmatized. The result of automatic lemmatization is checked mainly by Latin philologists during the post-lemmatization process. They correct unfitting assignments between text and lexicon or create new entries in the FLL to close gaps in the lexicon and in the lemmatization. To this end, human editors can use the so-called Lemmatization Editor of the eHumanities Desktop. It presents lemmatized text in a color code and a statistical overview indicating the state of lemmatization. The color code differentiates nine distinct levels of lemmatization. The code does not only lead the human editor to tokens that still need to be identified or disambiguated but also marks lemmatization results with different degrees of certainty. The expert then opens the so-called Word-Link-Editor for a token to check the tagger's choice that the expert can confirm or correct. Here, she or he can disambiguate the result if the tagger has not taken a decision. In very few cases -concerning mainly proper names, OCR mistakes or abbreviated wordforms -the color code displays the token in blue which means that no corresponding entry could be found in the FLL. In this case, the human editor can either correct the misspelling directly within the Lemmatization Editor or create a new entry. If necessary, the expert can leave the editor and open the Lexicon Browser to create a new superlemma and/or expand lemmata. All these actions influence the state of lemmatization directly which becomes visible through the changing color code.
Evidently, the manual post-lemmatization process may detect errors in the FLL. In this case, the expert must correct the corresponding entries, merge duplicates, or delete incorrect entries. Such errors are mainly due to the initial setup when information was taken from different sources or through overgeneration as a result of morphological expansion. But even human editors sometimes make mistakes. Therefore the lexicon offers the possibility to mark entries as double-checked. Since changes in the lexicon cannot simply be undone, changing the entries requires a high level of expertise, which is why the lemmatization and subsequent lexicon work is only carried out by trustworthy project members. As a result, this work is done using a limited crowdsourcing approach by assigning update rights to a limited number of experts using eHumanities Desktop's rights management tool (Gleim et al., 2012).
In the project Computational Historical Semantics 21 , linguists from the universities of Bielefeld, Regensburg and Tübingen, who worked directly with the TTLab and the Historical Seminar of the Goethe University, were allowed to update the lexicon. In addition, external partner projects from the Universities of Cologne, Freiburg and Mainz participate in the update process of the FLL after appropriate training. 22 As of May 2019, the percentage of lemmata created or modified by this procedure was 13.93% (18,565 lemmata). The synchronization between the lemmatized corpus and the FLL as induced by post-lemmatization and lexicon updates is managed by TEILex, a system for integrating lexica and text corpora, in which the tokens of a corpus are linked with corresponding lexicon entries in such a way that lexicon updates are immediately transferred to the linked corpora and vice versa. In this way, expert-based lexicon modeling becomes less dependent of indexing the underlying corpus. Figure 2 shows the corresponding workflow of TEILex: automatic text processing using TextImager (which generates XMI files) and subsequent TEI conversion generates a TEI-conform corpus that is indexed and synchronized with the FLL using TEILex. In this process, experts can change both the lexicon (see update (1) in Figure 2) and the tagged corpus (update (3)). However, in order to prevent the corpus from being re-indexed after each change, the synchronization of the TEILex corpus with the FLL allows the automatic execution of changes of the lexicon on the synchronized corpora. The TEILex index is then automatically revised without having to interrupt the use of the system. This procedure has greatly accelerated the post-correction process by protecting it from too many interruptions.

Lemmatization in the Flux
In Eger et al. (2016) and Gleim et al. (2019), we experimented with different lemmatizers on Latin datasets. Since the publication, thanks to the emergence of large transformer networks (e.g. BERT; Devlin et al., 2019), significantly better models have appeared, which prompted us to update our results. These transformer networks are large language models that are trained on even larger amounts of data and recognize and process syntactic and semantic relations (Clark et al., 2019). These attentionbased networks with up to 24 layers and over 350 million parameters (sometimes even more; see Shoeybi et al., 2019) are trained on natural language texts (mostly Wikipedia and digital books) with the task of reconstructing deleted words by their context. These models can then be adapted to specific tasks on the basis of training data (Kondratyuk and Straka, 2019). BERT (Devlin et al., 2019) is still the most popular model, as it was pre-trained in 104 languages and is publicly available. In addition, the models are not too large, so that they can be (post-)trained for individual purposes without the need for special hardware. The most advanced lemmatization model currently available from this class of approaches is UDify (Kondratyuk and Straka, 2019), which is based on a multilingual BERT model. UDify has been fine-tuned on 124 treebanks in 75 languages and is capable of tagging universal POS 23 , morphological features, lemmata, and dependency trees and also obtains acceptable results in unknown languages. Under these 124 treebanks are 3 in Latin: Proiel (Haug and Jøhndal, 2008), Perseus (Bamman and Crane, 2011) and ITTB (Cecchini et al., 2018;Passarotti and Dell'Orletta, 2010) with a total of 582,336 tokens. The Proiel treebank contains most of the Vulgate New Testament translations plus selections from Caesar and Cicero. ITTB (i.e. the Index Thomisticus Treebank) contains the complete work by Thomas Aquinas (1225-1274; Medieval Latin) and by 61 other authors related to Thomas, while Perseus contains a selection of passages from diverse authors like Augustus and Tacitus. All treebanks are annotated with the Universal Dependencies 24 (UD) Framework (Nivre et al., 2016). In order to adapt UDify, the texts are preprocessed with the help of BERT, whereby for each downstream task a separate classifier is trained with the help of the resulting BERT word vectors. The UDPipe model (Straka and Straková, 2017), on the other hand, is a pipeline system (i.e. a system that interconnects series of NLP tools) that does not rely on large transformer models, but is designed independently for each target language and therefore cannot (directly) exploit similarities between languages. As a consequence, 97 independent models were trained on 97 treebanks from 64 languages (Straka and Straková, 2017).
This analysis shows that in-domain lemmatization can be delegated to modern neural network models that appear to be largely independent of lexicons of the type of the FLL. Even those models of Gleim et al. (2019), which use the FLL, are outperformed by transformer-based models in the area of outdomain lemmatization. From the point of view of the manual post-lemmatization process, however, the reference to a lexicon remains indispensable when it comes to distinguishing between lemmata and superlemmata and correctly assigning them to incorrectly lemmatized tokens -a residual task that can probably never be fully automated. That is, regardless of the enormous progress achieved by transformer-based taggers, an out-domain F-score of 88% (as demonstrated by UDify on the Capitularies) falls short of the threshold that would be acceptable from the point of view of humanities scholars. And even if one assumes that F-scores around 98% are practically unattainable, since inter-rater agreements also fall short of this margin, the requirement remains for human post-processing and especially post-lemmatization, which requires corresponding lexicon-based guidance as addressed, for example, by the FLL. And since a lexicon such as the FLL requires integration with distributional semantic resources sprouting up everywhere, an answer is needed to the question of how the compact vector representations of such approaches can be mapped to manageable graphs, which in turn can be 25 The F1 score is the harmonic mean of precision and recall of the corresponding classification. consulted by expert users to refine their lexicon work. An answer to this question will be sketched in the following section.

Genre-sensitive Embeddings in Latin
Gleim et al. (Gleim et al., 2019) show that using word embeddings can boost lemmatization also in Latin to a remarkable degree. However, the embeddings involved are calculated for large corpora, such as the Patrologia Latina (Jordan, 1995), without taking, for example, the genre-related diversity of text vocabularies into account: as with lexical ambiguities, such embeddings model varieties using composite structures without providing separate representations for them. Apparently, approaches of this sort assume that resources are homogeneous data: they operate on as much data as possible from genres, registers or time periods that do not exhibit substantial heterogeneity or whose heterogeneity is ignored by the model. In this article, we take a different approach: by subdividing corpora according to their contextual stratification, we obtain subcorpora for training specialized embeddings that differentiate knowledge which is otherwise amalgamated within the same model. This makes it possible to explicitly represent differences of the same word due to its varying use in different genres, subject areas or stylistic contexts. In this way, reference is made to linguistic knowledge in order to make the computation of lexical relations more transparent. The further goal is to improve the interpretability of machine learned resources from the point of view of the targeted community.
Thus, in light of Section 1, our goal is to extend FLL so that for each word a series of embeddings is learned that are differentiated according to a subset of contextual dimensions (e.g. author, genre, style, register, topic, etc.). FLL then no longer represents words (superlemmata, lemmata, wordforms or syntactic words) as nodes of a monoplex network, but as nodes of a multiplex network (Boccaletti et al., 2014) whose multiplexity is established by context dimensions. Henceforth, we denote this variant of FLL by FLL+: FLL+ is a terminological ontology that spans a multiplex network according to different contextual dimensions and thus provides a series of contextualized representations for each of its lexical entries -as (downloadable) embeddings and as traversable SemioGraphs (see below). Multiplicity means to network the vertices of the same graph according to different (in the present article: contextual, discourse-level) criteria. Furthermore, the embeddings used to network FLL+ as a multiplex network are partly hierarchically ordered by the subset relations of the corpora involved. This means, among other things, that word embeddings calculated for a subcorpus X of a corpus Y can be used to approximate embeddings calculated on the latter.
In order to generate FLL+, we consider genre as a contextual dimension by example of six instances (see Table 5 Table 5) that contains the Corpus Iuris Civilis (compiled 528-534) and the Corpus Iuris Canonici (gradually compiled from the mid-12th to the 15th century) next to canonical decrees and Carolingian law texts. This approach of contrasting the reference corpus with specialized subcorpora makes it possible to compare embeddings generated by means of the reference with those obtained for specialized genres (see Section 6 for such a comparison).
Since our focus is on genre and author-related variation and not on method optimization, we concentrate on efficiently computable methods: 1. We utilize the well-known continuous bag-of-words (CBOW) and the skip-gram model of word2vec . 2. As a further development of word2vec, we experiment with fastText (Joulin et al., 2017), which additionally evaluates character embeddings for computing word embeddings. This approach again comes in two variants: skip-gram and CBOW. 3. As we deal with subsets of corpora of varying size (see Table 5), we also experiment with a method that addresses "small" corpora. This relates to the approach of Jiang et al. (2018), who evaluate the common absence of words in text segments as an additional source of information (cf. Rieger 1989). An alternative approach is the one of Silva and Amarathunga (2019), who generate random paths in sentence networks to obtain sentence variants for extending small input corpora. Both approaches have been evaluated in the context of word similarity tasks and are promising candidates for dealing with low-resource situations. However, we concentrate on the approach of Jiang et al.  Since our goal is not only to generate distributional semantic resources for Latin text genres, but to make them also interactively available to the (expert) user, we finally generate so-called Semio-Graphs 29 as visualizations of local graph views using the pipeline 30 of Figure 3. It includes three steps to process plain text, TEI or XMI 31 documents where TextImager (Hemati et al., 2016) is used to preprocess input documents based on the procedures for processing Latin documents described in Gleim et al. (2019): preprocessing creates training files from single documents or entire repositories as input for vectorization (e.g. by means of word2vec etc.). The third step (graphicalization) generates input files required by the SemioGraph application, a Python script that uses the flask server framework, which is available as a Docker image for rapid setup. The SemioGraph application manages all generated embeddings for generating graph views. The output of graphicalization is stored in Python shelve files, that is, key value stores of Python objects that allow for fast access by the server. If available, graphicalization enriches SemioGraphs on the node level with topic labels using text2ddc (Uslu et al., 2019), which is currently trained for 40 languages (but not yet for Latin). The Python shelves are finally used to generate interactive visualizations using SemioGraph's web interface. In this interface, node size codes two dimensions of vertex-related salience: while height codes degree centrality, width is used to code the similarity to the seed word. Furthermore, node transparency can be used to code degrees of class membership values, while node color can map the corresponding class (this feature is not used in our example below). Beyond that, border color can be used to code a 2nd-level vertexrelated classification (i.e. topic-related class membership). Finally, in the case of multilabel classifications, node tiling (i.e. pie charts) can be used to code distributions of class membership per vertex (also this feature is currently not used by our Latin SemioGraphs).
Generally speaking, a SemioGraph is a visual, interactive representation of word embeddings as a result of the latter procedure: starting from a word x, its SemioGraph displays those m words which by their embeddings are among the first m neighbors of x in the similarity space induced by the underlying embeddings. This means that vertices or nodes in a SemioGraph represent words or multi-word units, while edges or links represent associations of these nodes, the strength of which is represented by the thickness of the (visual representation of the) edges. Since word embeddings induce fully connected graphs (in which all words are connected with each other), the SemioGraph interface allows to filter low associations to get visual insights into the underlying graph structures: this enables the visual formation of clusters of nodes, which have a higher number of internal associations than to members of other clusters or to outliers. Thus, if a SemioGraph of a word x is generated using this method, this does not mean that visually disconnected words are not associated. Actually, they are, but to a degree below the user-controlled threshold value. In any case, due to the way embeddings are calculated here, SemioGraphs show paradigmatic associations. This means that even if word co-occurrences are frequent (indicating higher syntagmatic associations), the word associations need not appear in the corresponding SemioGraph. This happens in cases where the contexts in which the words are used throughout the underlying corpus are less similar than their inclusion among the m most similar neighbors would require: a SemioGraph always shows only a selected subset of associations. Thus, not appearing in a given SemioGraph does not necessarily mean non-existence. If the latter selection would include all words from the input corpus, then these would all be displayed in the SemioGraph, of course by means of edge representations of variable thickness. Visualizing genre-sensitive embeddings using SemioGraphs then means first generating word embeddings separately for corpora that reflect certain genre-, register-, chronology-, or other context-related features, and then visualizing the neighborhoods of certain seed words to determine the differences or similarities of their context-sensitive syntagmatic or paradigmatic associations. This is illustrated in detail in the next section by means of paradigmatic word associations.

Brief Case Studies in Computational Historical Semantics with the Help of SemioGraph
In this section, we apply the method of local graph views to Latin word embeddings as provided by SemioGraph, briefly present three SemioGraphs and sketch how they may provide a new kind of evidence for computational historical semantics in the humanities. In our first example we calculate paradigmatic associations (Rieger 1989) of the noun conclusio (conclusion) in the test corpus of legal texts (see Table 5). The resulting SemioGraph (see Figure 4) allows first observations on the principle func-tionality of SemioGraphs for a comparatively small corpus, on the potentials of genre-sensitive Semi-oGraphs, and at the same time on necessary further work and current performance of the FLL and TTLab's tagger and lemmatizer for Latin.
On the one hand, the calculated semantic connections in Figure 4 correspond to what for historians fits very well into a well-known context of legal history. First, there are mostly technical terms for different aspects in legal processesinquiry, examination, excuse, allegation (cognitio, examinatio, excusatio, denuntiatio, etc.) -from the dispute to its settlement. Time expressions such as ten-year (decennius) or four-year (quadriennium) may not necessarily refer to the length of punishments, but rather to time specifications in legislation. Secondly, there is a single recognizable content, that is, marriage legislation, and this is quite clearly visible. The graph shows a vocabulary that historians would expect in texts on marriage legislation of these centuriesdivorce / repudiation (divortium, repudium), copulation or copulate (the term copulam signals the need for manual post-lemmatization), the legal importance of the "consumption" of a marriage (consummatio), puberty (pubertas), the conjugal union (matrimonialis -adjective), or conceiving and childbearing (partum, specification via post-lemmatization is needed).
On the other hand, links between many words -as well as the occurrence of words without any linksindicate a need for further clarification, which must be systematized in the workflow of such queries: no link or edge connects the keyword conclusio with any of the other words. Apparently, conclusio does not co-occur in the underlying corpus with any of these words with sufficient frequency. Links between words require a certain minimum number of neighborhoods, which serve as a reliable source of information for their paradigmatic associations. Note again that if a SemioGraph shows no link between two words, this does not mean that they are not related to each other; it only means that their paradigmatic association is below a certain minimum, where the user of the SemioGraph sets this threshold him-or herself.
Some of the key words of marriage legislation such as copulation or consumption are also disconnected in the SemioGraph in Figure 4. Some of these observations may disappear with the enlargement of the underlying corpus by means of texts that provide more evidence about their contextual similarities (Miller & Charles 1991). In any case, the calculation of paradigmatic associations ultimately aims at making such phenomena visible. That is, associations should become visible even if the words involved are rare in the underlying corpus, but the similarities of the contexts in which they are used are sufficiently strongly confirmed by that corpus. It is therefore less a matter of eliminating such observations in a SemioGraph (in terms of post-correction) than of making them (1) controllable by means of corpus selection and (2) interpretable with respect to this selection. One of several possible explanations may be that the keyword has not been used in standardized collocations: and such an observation can then be the starting point for research in the respective humanities.    Catholic creed. The strong connection of the word unigenitus to consubstantiality ("of the same substance", consubstantialis, consubstantialiter) indicated by the edges in the SemioGraph stresses the link to the Catholic creed. The SemioGraph reflects the prevailing religious attitude towards paternity bonds which subordinated carnal to spiritual paternity. Fatherhood of God, priests and godparents was a strong discursive element. The important role of godparents is visible by means of the terms cofather and co-mother (commater/conmater and compater/conpater) as neighbors of pater. Less visible are clerics as spiritual fathers since they were simply addressed as father. Significant is also the lack of a broad family vocabulary that would differentiate family relationships. Only the mother and the paternal uncle (patruus) are present in this SemioGraph. This observation coincides at first glance with the broadly discussed hypothesis that the Roman male agnatic kinship system faded away under the influence of the church from the sixth century on in favor of kin groups organized around the conjugal couple (see Jussen, 2009). There is hardly any evidence of genealogical connections and far-reaching family relationships in this graph. This will be different in the SemioGraph in Figure 7. Odd occurrences -such as the rarely used proper name Sopater as part of the SemioGraph in Figure 6 -immediately raise doubts and are hence particularly important terms for cross-checks of automatic procedures, that is, for intellectual post-correction. Sopater, correctly lemmatized in the FFL as a proper name, is an obvious candidate to check how paradigmatic similarities and corresponding neighborhoods are calculated. Even more conspicuous is the rather central position of the term paterfamiliae, a very rare variant of the common but here completely missing lemma paterfamilias. Both are subsumed under the same superlemma within FLL. In any event, two-dimensional geometric representations of graphs should not be overinterpreted -they may be due more to the visualization method and less to the underlying graph topology.
Such obvious but rare problems, however, are contrasted in Figure 7 by a multitude of plausible and interpretatively controllable links, so that the added value of paradigmatic graphs for Latin texts can be regarded as successfully tested alongside the classical analyses of co-occurrences and syntagmatic patterns. First of all, it is striking that the kinship designations and the distinctions between the maternal and paternal lines, which were missing in the first graph, are prominent here (avus -grandfather, proavus -great-grandfather, propatruus -great-granduncle, abpatruus -great-great-great uncle from the father's side, a very rare word by the way, tritavusa grandfather's great-grandfather, proavunculus -great uncle from the mother's side). It is also striking that the designations almost exclusively refer to the paternal line. Since canon law has developed the kinship designations in both lineages in detail (in connection with the prohibition of incest), this finding again requires verification, that is, further research by the humanities scholar. In this case, the compilation of the corpus probably needs to be corrected. Presumably, charters function linguistically differently from normative legal texts so that the corpus of legal texts should be divided into two corpora in future work. Furthermore, the data of the SemioGraph will probably only become meaningful when the long-term diachronic corpus of legal texts can be examined according to time sections. Only then will it be possible to see what was different in the Roman Mediterranean world compared to the post-Roman Latin-Christian societies.
These sh SemioGr

2013).
Since these case studies show the need for downstream processes of close reading and possibly for corrections of the underlying lemmatization, we have identified in this process chain an instance for a digitally enhanced hermeneutic circle. It could be seen as an example of a prototypical strategy for dealing with lemmatization or, more generally, natural language processing of historical language texts. Future work will focus on a more detailed examination of word embeddings in Latin, their local and global graph representations, and in particular on their intrinsic evaluation.