Software-gestütztes Arbeiten mit Historischen Texten Text Mining in den Geisteswissenschaften. Text Re-use, Knowledge Transfer, and Applications

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1 Software-gestütztes Arbeiten mit Historischen Texten Text Mining in den Geisteswissenschaften Text Re-use, Knowledge Transfer, and Applications Martin-Luther-Universität Halle/S. Halle/S., 2011/01/27 Natural Language Processing Group Department of Computer Science University of Leipzig

2 Agenda Text re-use from different points of view.. of a computer scientist, of a digital humanist, of an e-humanist and of a humanist 6 levels of text re-use or quotation mining Some bird's eye view results and obvious problems Fragmentary authors 2

3 Introduction 3

4 More General Knowledge Transfer (Biology) Similarity of branches of the same knowledge, knowledge is changing over time 4

5 More General Knowledge Transfer (Archaeology) Functionality of objects vs. object of interest, critical amount of reuse 5

6 More General Knowledge Transfer (Further aspects) Software: (PRO): Modular programming, (CON): Legal right problems Music: (CON): Plagiarism Images: (PRO): Object recognition Properties of Text Reuse: Efficiency, cost reduction Plagiarism (least effort) vs. knowledge transfer (modern: research, ancient: philosophical debate) Level of text modification Properties of Ancient Text Reuse: Fragmentary texts Fragmentary authors In e. g. medieval ages: Monks copy texts (sometimes with errors) Ancient authors reused text passages. However including modifications: Language evolution dialects 6

7 Definitions/Terminology Citation/quotation Modern: (SOURCE, <TEXT>) Ancient: mostly (, <TEXT>) Plagiarism Modern: mostly (, <TEXT>) Text Reuse Legal right aspects are ignored: For this reason: (<TEXT>) Literal citations Parallel texts Paraphrases Text Reuse graph G=(V,E), V is set of sentences, E set of links between elements of V (Hyper-textual structure in a Digital Library) 7

8 Language vs. Communication 8

9 Different research interests Humanities ehumanities Digital Humanities Computer Science 9

10 Text Re-use A computer scientist's point of view 10

11 A computer scientist thinks in language models -... in pseudo algorithms -... about complexity reduction of algorithms -... about efficient text re-use models 11

12 Complexity of algorithms Naive method: comparing every sentence with all other sentences TLG: 5,500,000*5,500,000 = 3.025e13 comparisons Assumption: Comparison rate of 1000 sentences/sec. This process would run about 3.025e10 seconds or more than 959 years. Even if we would compare only sentences with all significant phrases we would need about one year. That's why: Usage of divide & conquer strategies Intelligent pre-clustering of data Using occurrences of Plato, work titles or roles of Plato's works Using significant terms of Plato's work 12

13 Pseudo algorithm for Text Reuse 1 2 V = segment_corpus(c) with v1, v2,..., vn V, vi=c and vi vj for each vi V 3 4 Fi=train_features(vi); for each vi V Training for each fk Fi ei=(vi,vj) E=select all vj containing feature fk Linking for each ei E si=scoring(ei=(vi,vj) E; Fi; Fj); if(s <threshold){e=e\{e }} i Scoring i 13

14 Types of Completeness of Text Re-use algorithms Extraction of fragmentary authors String approaches: GST Letter n-grams Syntactic approaches: Longest Common Consecutive Words Word n-grams Distance based co-occurrences Semantic approaches: Semantic clustering Semantic graph based approach(es) Latent relations Radius retrieval More complex approaches: DCT 14

15 Literal quotations: Longest Common Consecutive Words αἱ δ' ἐν ταῖς γυναιξὶν αὖ μῆτραί τε καὶ ὑστέραι λεγόμεναι διὰ τὰ αὐτὰ ταῦτα ζῷον ἐπιθυμητικὸν Step: Iterative training of possible n-grams candidates (Training) αἱ δ' αἱ δ' ἐν αἱ δ' ἐν ταῖς αἱ δ' ἐν ταῖς γυναιξὶν Removing all n-grams having a smaller frequency of 2 and having less than 5 words (Selection) 3. Removing prefixes and suffixes 4. Creating inverted list for the above detected n-grams (Mapping n-gram to sentence) 5. Collect all sentences having same n-grams (Citation candidates) 6. Compute similarity by word overlap (Dice) 15

16 Semantic quotations: a graph based approach I Basic assumption: Two words being semantically similar have the same co-occurrences Example: laptop: {mouse, battery, display, portable} computer: {mouse, keyboard, display, mainframe} Algorithm (simplification): Compute co-occurrences for all words Compare co-occurrence profiles of two words Compute intersection I of co-occurrence profiles of both words Compute union U of co-occurrence profiles of both words Compute ratio sim=card(i)/card(u) Example (continued): I={mouse, display} U={mouse, battery, display, portable, keyboard, mainframe} sim=card(i)/card(u)=2/6=1/3 16

17 Semantic quotations: a graph based approach II 17

18 Semantic quotations: a graph based approach III Toy sample corpus 1. Copy from one, it's plagiarism; copy from two, it's research. 2. Plagiarism is not the same as copyright infringement. 3. Plagiarism is to to copy from one but to copy from two is research. 1. Step: Co-occurrence analysis 3. Step: Graph based similarity Intersection 2. Step: (copy,from, 1.0) (copy,from, 1.0) (copy,from) (copy,one, 1.0) (copy,one, 1.0) (copy,one) (copy,it's, 1.0) (copy,it's, 1.0) (copy,it's) (copy,plagiarism, 0.8) (copy,plagiarism, 0.8) (copy,plagiarism) (copy,research, 1.0) (copy,research, 1.0) (copy,research) (plagiarism,from, 0.8) (plagiarism,from, 0.8) (plagiarism,from) (plagiarism,one, 0.8) (plagiarism,one, 0.8) (plagiarism,one) (plagiarism,it's, 0.8) (plagiarism,it's, 0.8) (plagiarism,it's) (plagiarism,copy, 0.8) (plagiarism,copy, 0.8) (plagiarism,copy) (plagiarism,research, 0.8) (plagiarism,research)... (plagiarism,research), (copy,copyright, 0.1) 4. Step: Selection Selection... (plagiarism,copyright) (plagiarism,infringement) 18

19 Shannon's Noisy Channel Theorem vs. Kolmogorow Complexity Shannon: Kolmogorow: Text A Text A' Min. Program P Research question: Dissimilarity of A und A' in order to become LINKED. If LINKED: Which min progs make no sense? Halstead metric and McCabe metric 19

20 Text Re-use and quotations A Digital Humanity's point of view 20

21 A digital humanist thinks how to store textual data in order to preserve all necessary information such as described by Leiden Conventions, additional comments and so on - Typically a digital humanists tend to use XML for this task like TEI P5, TEI MSS, or EpiDoc -... how to annotate additional information such as person names (fragmentary authors) or locations -... how to combine textual data and mining data (manual or automatized) such as quotations 21

22 Text re-use: Where to set the start and end tag of an XML annotation? αἱ μῆτραί τε καὶ ὑστέραι λεγόμεναι... αἱ δὲ ἐν ταῖς γυναιξὶν μῆτραί τε καὶ ὑστέραι λεγόμεναι... αἱ δ' ἐν ταῖς γυναιξὶν αὖ μῆτραί τε καὶ ὑστέραι λεγόμεναι... αἱ δ' ἐν ταῖς γυναιξὶ μῆτραί τε καὶ ὑστέραι λεγόμεναι... 22

23 How to annotate iff there are overlaps? I - A simple example (Source; Lexicography has shown little sign of being affected by the work of followers of J.R. Firth, probably best summarized in his slogan, You shall know a word by the company it keeps (Firth, 1957). - There are two chances of annotating the quotation: - document internal annotations (often used by humanisists) such as Lexicography has shown little sign of being affected by the work of followers of J.R. Firth, probably best summarized in his slogan, <quote>you shall know a word by the company it keeps</quote> <ref>(firth, 1957)</ref> - document external annotations (mainly used by computer scientist for reasons of readability) such as - in GraphML - Canonical Text Services - RDF 23

24 How to annotate iff there are overlaps? II - Toy sample sentences: A B <quote source=1>c D E <quote source=2>f G H I J</quote> K L</quote> - Intra document annotations does not work (without XML hacks) - Inter document annotations can deal with those kinds of problems such as CTS, GraphML 24

25 Text Re-use in 6 steps An ehumanities point of view 25

26 An e-humanist thinks... - about infrastructure (combining several textual resources) - about Humanities Computing (problem focused improvements of algorithms) 26

27 6 levels of text re-use - Level 1: Pre-processing - Level 2: Feature training - Level 3: Feature selection (Fingerprinting) - Level 4: Linking - Level 5: Scoring - Level 6: Post-processing 27

28 Level 1: Pre-processing - Capitalisation (e. g. all letters to lowercase) - Normalisation (e. g. removing all diacritics) - Lemmatisation (e. g. replace inflected words by baseform) - Synonym replacements (e. g. replace a word by the most common (most frequent) synonym) - String similarity (words that are similar written) Result: Cleaned text 28

29 Level 2: Training A general overview - training means to identify the feature of a re-use unit such as - letter level (1. generation text re-use models, starting from 1970s) such as - letter n-gram features - lexical level (2. generation, within 1990s) - syntactial such as word n-gram features - semantical such word features 29

30 Level 2: Syntactical training details Syntactical feature N-gram feature Overlapping Shingling Longest Common Consecutive Words Property of overlapping features Non overlapping Local hash breaking Property of constant or variable n-gram length Global hash breaking 30

31 Level 3: Selection of training data building a re-use fingerprint - local selection strategies work with the knowledge within a re-use unit such as - Local 0 mod p (e. g. position of a word within a re-use unit) - random selection - Winnowing - global selection strategies work with global knowledge such as a word list of the entire corpus like - Global 0 mod p (e. g. rank of a word (cf. Zipfian law) ) - Selection of special word classes such as nouns - Inverted Document Frequency (IDF) score - Minimum feature frequency selection - Maximum feature frequency selection 31

32 Level 4: Linking types comparing re-use units Intra corpus detection (Text reuse): Inter corpus detection (Modern: Plagiarism, Ancient: e.g. bible): 32

33 Level 5: Scoring Similarity Word similarity Word resemblance Word containment Level of similarity Feature similarity Feature resemblance Feature containment Further measures: - Levenshtein distance - Log likelihood ratio - many more 33 Symmetric vs. asymmetric measures

34 Level 6: Post processing I Source (Plot): John Lee: A Computational Model of Text Reuse in Ancient Literary Texts, The same order is more trustworthy than a sole and highly similar link. 34

35 Level 6: Post processing A text re-use from a document with a high text re-use coverage is more trustworthy than from a less frequently re-used text. A text re-use from a section of a document with a high text re-use temperature is more trustworthy than from a less frequently re-used part of a document. 35

36 Accessing & visualisation of text re-use A Humanities' point of view 36

37 Literal citations: portal II 37

38 Literal citations: portal III 38

39 Literal citations: Visualisation I Plato: Timaeus 91b7 ff. αἱ δ' ἐν ταῖς γυναιξὶν αὖ μῆτραί τε καὶ ὑστέραι λεγόμεναι διὰ τὰ αὐτὰ ταῦτα ζῷον ἐπιθυμητικὸν ἐνὸν τῆς παιδοποιίας ὅταν ἄκαρπον παρὰ τὴν ὥραν χρόνον πολὺν γίγνηται χαλεπῶς ἀγανακτοῦν ϕέρει καὶ πλανώμενον πάντῃ κατὰ τὸ σῶμα τὰς τοῦ πνεύματος διεξόδους ἀποϕράττον ἀναπνεῖν οὐκ ἐῶν εἰς ἀπορίας τὰς ἐσχάτας ἐμβάλλει καὶ νόσους παντοδαπὰς ἄλλας παρέχει μέχριπερ ἂν ἑκατέρων ἡ ἐπιθυμία καὶ ὁ ἔρως συναγαγόντες οἷον ἀπὸ δένδρων καρπὸν καταδρέψαντες ὡς εἰς ἄρουραν τὴν μήτραν ἀόρατα ὑπὸ σμικρότητος καὶ ἀδιάπλαστα ζῷα κατασπείραντες καὶ πάλιν διακρίναντες μεγάλα ἐντὸς ἐκθρέψωνται καὶ μετὰ τοῦτο εἰς ϕῶς ἀγαγόντες ζῴων ἀποτελέσωσι γένεσιν αἱ δ' ἐν ταῖς γυναιξὶν αὖ μῆτραί τε καὶ ὑστέραι λεγόμεναι διὰ τὰ αὐτὰ ταῦτα ζῷον ἐπιθυμητικὸν ἐνὸν τῆς παιδοποιίας ὅταν ἄκαρπον περὶ τὴν ὥραν χρόνον πολὺν γίγνηται χαλεπῶς ἀγανακτοῦν ϕέρει καὶ πλανώμενον πάντῃ κατὰ τὸ σῶμα τὰς τοῦ πνεύματος διεξόδους ἀποϕράττον ἀναπνεῖν οὐκ ἐῶν εἰς ἀπορίας τὰς ἐσχάτας ἐμβάλλει καὶ νόσους παντοδαπὰς ἄλλας παρέχει μέχριπερ ἂν ἑκατέρων ἡ ἐπιθυμία καὶ ὁ ἔρως ξυναγαγόντες οἷον ἀπὸ δένδρων καρπὸν καταδρέψαντες ὡς εἰς ἄρουραν τὴν μήτραν ἀόρατα ὑπὸ σμικρότητος καὶ ἀδιάπλαστα ζῷα κατασπείραντες καὶ πάλιν διακρίναντες μεγάλα ἐντὸς ἐκθρέψωνται καὶ μετὰ τοῦτο εἰς ϕῶς ἀγαγόντες ζῴων ἀποτελέσωσι γένεσιν περὶ δὲ τῆς μήτρας ὅτι τε ζῷόν ἐστι καὶ αὕτη καὶ τὰ ἀπὸ τοῦ πατρὸς ἐξερχόμενα μόρια ταῦτα πάλιν λέγει Πλάτων αἱ δ' ἐν ταῖς γυναιξὶν αὖ μῆτραί τε καὶ ὑστέραι λεγόμεναι διὰ τὰ αὐτὰ ταῦτα ζῷον ἐπιθυμητικὸν ἐνὸν τῆς παιδοποιίας ὅταν ἄκαρπον παρὰ τὴν ὥραν χρόνον πολὺν γίνηται χαλεπῶς ἀγανακτοῦν ϕέρει καὶ πλανώμενον πάντῃ κατὰ τὸ σῶμα τὰς τοῦ πνεύματος διεξόδους ἀποϕράττον καὶ ἀναπνεῖν οὐκ ἐῶν εἰς ἀπορίας τὰς ἐσχάτας ἐμβάλλει καὶ νόσους παντοδαπὰς ἄλλας παρέχει μέχριπερ ἂν ἑκατέρων ἡ ἐπιθυμία καὶ ὁ ἔρως ξυναγαγόντες οἷον ἀπὸ δένδρων καρπὸν καταδρέψαντες ὡς εἰς ἄρουραν τὴν μήτραν ἀόρατα ὑπὸ σμικρότητος καὶ ἀδιάπλαστα ζῷα κατασπείραντες καὶ πάλιν διακρίναντες μεγάλα ἐντὸς ἐκθρέψωνται καὶ μετὰ τοῦτο εἰς ϕῶς ἀγαγόντες ζῴων ἀποτελέσωσι γένεσιν 39

40 Literal citations: Visualisation II Plato: Timaeus 91b7 ff. αἱ δ' ἐν ταῖς γυναιξὶν αὖ μῆτραί τε καὶ ὑστέραι λεγόμεναι διὰ τὰ αὐτὰ ταῦτα ζῷον ἐπιθυμητικὸν ἐνὸν τῆς παιδοποιίας ὅταν ἄκαρπον παρὰ τὴν ὥραν χρόνον πολὺν γίγνηται χαλεπῶς ἀγανακτοῦν ϕέρει καὶ πλανώμενον πάντῃ κατὰ τὸ σῶμα τὰς τοῦ πνεύματος διεξόδους ἀποϕράττον ἀναπνεῖν οὐκ ἐῶν εἰς ἀπορίας τὰς ἐσχάτας ἐμβάλλει καὶ νόσους παντοδαπὰς ἄλλας παρέχει μέχριπερ ἂν ἑκατέρων ἡ ἐπιθυμία καὶ ὁ ἔρως συναγαγόντες οἷον ἀπὸ δένδρων καρπὸν καταδρέψαντες ὡς εἰς ἄρουραν τὴν μήτραν ἀόρατα ὑπὸ σμικρότητος καὶ ἀδιάπλαστα ζῷα κατασπείραντες καὶ πάλιν διακρίναντες μεγάλα ἐντὸς ἐκθρέψωνται καὶ μετὰ τοῦτο εἰς ϕῶς ἀγαγόντες ζῴων ἀποτελέσωσι γένεσιν αἱ δ' ἐν ταῖς γυναιξὶν αὖ μῆτραί τε καὶ ὑστέραι λεγόμεναι διὰ τὰ αὐτὰ ταῦτα ζῷον ἐπιθυμητικὸν ἐνὸν τῆς παιδοποιίας ὅταν ἄκαρπον περὶ τὴν ὥραν χρόνον πολὺν γίγνηται χαλεπῶς ἀγανακτοῦν ϕέρει καὶ πλανώμενον πάντῃ κατὰ τὸ σῶμα τὰς τοῦ πνεύματος διεξόδους ἀποϕράττον ἀναπνεῖν οὐκ ἐῶν εἰς ἀπορίας τὰς ἐσχάτας ἐμβάλλει καὶ νόσους παντοδαπὰς ἄλλας παρέχει μέχριπερ ἂν ἑκατέρων ἡ ἐπιθυμία καὶ ὁ ἔρως ξυναγαγόντες οἷον ἀπὸ δένδρων καρπὸν καταδρέψαντες ὡς εἰς ἄρουραν τὴν μήτραν ἀόρατα ὑπὸ σμικρότητος καὶ ἀδιάπλαστα ζῷα κατασπείραντες καὶ πάλιν διακρίναντες μεγάλα ἐντὸς ἐκθρέψωνται καὶ μετὰ τοῦτο εἰς ϕῶς ἀγαγόντες ζῴων ἀποτελέσωσι γένεσιν περὶ δὲ τῆς μήτρας ὅτι τε ζῷόν ἐστι καὶ αὕτη καὶ τὰ ἀπὸ τοῦ πατρὸς ἐξερχόμενα μόρια ταῦτα πάλιν λέγει Πλάτων αἱ δ' ἐν ταῖς γυναιξὶν αὖ μῆτραί τε καὶ ὑστέραι λεγόμεναι διὰ τὰ αὐτὰ ταῦτα ζῷον ἐπιθυμητικὸν ἐνὸν τῆς παιδοποιίας ὅταν ἄκαρπον παρὰ τὴν ὥραν χρόνον πολὺν γίνηται χαλεπῶς ἀγανακτοῦν ϕέρει καὶ πλανώμενον πάντῃ κατὰ τὸ σῶμα τὰς τοῦ πνεύματος διεξόδους ἀποϕράττον καὶ ἀναπνεῖν οὐκ ἐῶν εἰς ἀπορίας τὰς ἐσχάτας ἐμβάλλει καὶ νόσους παντοδαπὰς ἄλλας παρέχει μέχριπερ ἂν ἑκατέρων ἡ ἐπιθυμία καὶ ὁ ἔρως ξυναγαγόντες οἷον ἀπὸ δένδρων καρπὸν καταδρέψαντες ὡς εἰς ἄρουραν τὴν μήτραν ἀόρατα ὑπὸ σμικρότητος καὶ ἀδιάπλαστα ζῷα κατασπείραντες καὶ πάλιν διακρίναντες μεγάλα ἐντὸς ἐκθρέψωνται καὶ μετὰ τοῦτο εἰς ϕῶς ἀγαγόντες ζῴων ἀποτελέσωσι γένεσιν 40

41 Likelihood vs. Witnesses (Taxi problem) How trustworthy is a set reuses for a source? 41

42 Micro view visualisation 42

43 Macro view visualisation 43

44 Macro view: Hands-on solutions Middle Platonism Neoplatonism 44

45 A brief summary Algorithms 45

46 Some results 46

47 Literal citations: Similarity thresholds Similarity distribution 20,00% 18,00% 16,00% Percentage of all references 14,00% 12,00% 10,00% Similarity 8,00% 6,00% 4,00% 2,00% 0,00% 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 Similarity score of textual references 47 1

48 Similarity thresholds for some authors Similarity distribution of textual references separated by authors PLATO (4. BC) PLUTARCHUS (2. AD) GALENUS (2. AD) PORPHYRIUS (3. AD) CYRILLUS (5. AD) EUSEBIUS (4. AD) THEODORETUS (5. AD) STOBAEUS (5. AD) PROCLUS (5. AD) JOANNES PHILOPONUS (6. AD) SIMPLICIUS (6. AD) Frequency Similarity of textual references First result: As time passes, text reuse by later authors becomes much less literal. 48

49 Shannon's Noisy Channel Theorem Main challenge: How to model the noisy channel? OR Extract and systematise relevant noisy like Evolutionary changes: Language evolution Dialect changes Paraphrases Editorial changes Other changes: Fragmentary words Reordering of sentence structure 49

50 Distance in time vs. Text re-use similarity: Aristoteles & Plato Source: Maria Moritz: Informationsextraktion in den Altertumswissenschaften Fragmentarische Autoren - Extraktion altgriechischer Eigennamenund Belegstellen auf antiken Texten, 2011.

51 Within different text sorts (genre) and time slices there are not the same re-use styles!! 51

52 Problems with language model and text re-use 52

53 20 years of Fall after the Berlin Wall (Leipzig, Berlin) We are the people! 53

54 Some results problem focused What is a good similarity threshold (literal citations)? Dissimilarity vs. Fragments Plato: Low threshold provides good results as well Atthidographers: Poor quality precision less than 20% Multi word expressions like King Alexander the Great (literal citations) Phrases τοῦ Κυρίου ἡμῶν Ἰησοῦ Χριστοῦ (Engl.: in the Name of Our Lord Jesus Christ) Again: We are the people! Editorial references to publications Works in different editions - Embedded text reuse (relation between linking and scoring) - Detection boundaries of text reuse We are the people! 54

55 Evaluation Most critical point since there is no gold standard on ancient texts but... Modern texts: PAN corpus (literal text reuse), MeTeR corpus (semantic text reuse),? Ancient texts: Own gold standard by Extraction of text fragments (method for fragmentary authors) Extraction of editorial references Manual annotations by our researchers Negative evaluation by several randomised corpora 55

56 Basic research questions: some interdisciplinary aspects Syntactic level (text reuse): Taxi problem: Likelihood vs. witnesses How often a text passage have to be reused to exist nowadays? (similar to Gene) Modelling the noisy channel Semantic level (knowledge transfer): Stability vs. reuse vs. significance Volatility as reuse killer Minimum reuse unit (Testing distributional semantics assumption) Critical point for completeness of documents (interest)? Further research questions: Relation between text reuse and gnomology Influence of different kind of normalisations of text 56

57 Fragementary authors 57

58 What is a fragment? (Oxford English Dictionary, s.v. fragment) a part broken off or otherwise detached from a whole a part remaining or still preserved when the whole is lost or destroyed an extant portion of a writing or composition which as a whole is lost a portion of a work left uncompleted by its author Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

59 Different kinds of fragments material textual fragments fragments Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

60 material fragments material fragments = physical remains of ancient evidence reconstruction of the monument Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

61 textual fragments (1) textual fragments = material fragments bearing textual evidence surviving broken off pieces of ancient writings Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

62 Task 1: Workflow person name extraction Step 1: Extraction of candidates by pattern such as VN VN VN ETH VN LOC Pattern Step 2: Resolving morphological dependencies using Morpheus Step 3: Statistical evidence criterion Pattern Unsupervised Step 4: Generating a similarity graph of those candidates and building valid concept classes Unsupervised Step 5: Applying validated patterns on text in order to extract less frequent occurrences Step 6: Iterating step 2-5 Supervised Bootstrapping Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

63 Task 1: Some results of the PN extractor Step 1: Extraction of candidates by pattern such as Ἑλλάνικος Λέσβιος (VN ETH) Step 2: Resolving morphological dependencies - Removing candidates like Ἑλλάνικος Ἀκουσιλάῳ VN VN Step 3: Statistical evidence criterion like min freq is 4. Step 4: Generating a similarity graph of those candidates and building valid concept classes e.g. Ἑλλάνικος Λέσβιος (VN ETH) Ἑλλάνικος ὁ Λέσβιος (VN ZN ETH) Step 5: Applying validated patterns on text in order to extract less frequent occurrences Ἑλλάνικός τε ὁ Λέσβιος Ἑλλάνικος δὲ ὁ Λέσβιός Λέσβιος Ἑλλάνικος... Overall after 1 iteration 16 different versions of Hellanicus of Lesbos Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

64 Task 2: Extraction of fragments: Role of named entities Argumentation trail properties Graph properties w_id>=100 w_id>=300 w_id>=500 Complete graph && freq(word) && freq(word) && freq(word) >1 >1 >1 Named Entities Normalised Named Entities Normalised Text and Named Entities 538, , , ,618 1,149 4,487 2,178 57,762,474 34,818,138 25,615,956 21,004,538 15, , ,856 30,382,422 21,739,476 17,687,582 15,462,940 14,876 69,858 84, Average degree Number of trails > 108 > 108 > 108 > Average degree Average degree of internal node (trail length 2) Number of nodes Number of cooccurrences Number of significant co-occurrences Percentage Average degree of internal node (trail length 3) Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

65 Some results: Occurrence of author's name as fix points TLG v0.1 number of words sum of frequencies: Rank Word 741 Πλάτων 1555 Πλάτωνος 3122 Πλάτωνα 3612 Πλάτωνι Πλάτων Πλατωνικῶν Πλάτωνος ΠΛΑΤΩΝΟΣ Πλάτωνα Πλατωνικὸν Πλατωνικοὶ Πλατωνικῆς Πλατωνικὸς ΠΛΑΤΩΝ Πλάτωνός Πλάτωνι Πλατωνικοῦ Πλάτωνά TLG v Freq. Rank Word Πλάτων Πλάτωνος Πλάτωνα Πλάτωνι Πλάτωνός Πλάτωνά Πλάτωνοϲ Πλάτωνί _Πλάτων Πλάτωνες Πλάτωνας Πλάτων_καὶ Πλάτωνο Πλάτων Πλάτων_βούλεται Πλάτων_τὸν Πλάτων_ἐν Πλάτων_ὁ TLG v Freq. Rank Word Πλάτων Πλάτωνος Πλάτωνα Πλάτωνι Πλάτωνός Πλάτωνά Πλάτωνοϲ Πλάτωνί Πλάτωνες Πλάτωνας Πλάτωνο Πλάτωνε Πλάτωνάς Freq

66 Task 2: Extraction of fragments: Possible ways? Option 1: Statistical based Option 2: Pattern based Option 3: Completely different? Supervised Pattern Unsupervised Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

67 Workflow extraction of citations Step 1: Buliding significant patterns including author name, which is nominative and define a classification - FN V REF - FN PRE TIT V - FN PAR PRE TIT REF FN V PRE PAR TIT REF > > > > > > Pattern firstname verb preposition particle work reference Step 2: Resolving morphological dependencies between noun and verb Unsupervised Step 3: Validate the patterns against a standardized fragmentary author Supervised Step 4: Adapt patterns Pattern Source: Maria Moritz: Informationsextraktion in den Altertumswissenschaften Fragmentarische Autoren - Extraktion altgriechischer Eigennamen und Belegstellen auf antiken Texten, 2011.

68 Results of the citations extractor Step 1: Building significant patterns and a classification - Πλάτων ἔφη ( Phaed. 60b ) (FN V REF) FN V PRE PAR > TIT REF > Πλάτων (Plato) > ἔφη, φησι (wrote) > ἐν (in) καὶ (like) > Πρωταγόρᾳ (Protagoras) > ( Phaed. 60B ) Step 2: Resolving morphological dependencies between verb and noun - Removing candidates like Ἰησοῦν ἔφη (FN[acc] V[nom] ) Step 3: Validate patterns against a standardized fragmentary author - remove/improve patterns which don't work well FN PRE TIT (no known titel borders ) - use known titles to improve Step 4: Adapt patterns Source: Maria Moritz: Informationsextraktion in den Altertumswissenschaften Fragmentarische Autoren - Extraktion altgriechischer Eigennamen und Belegstellen auf antiken Texten, 2011.

69 Again: textual fragments Athenaeus, Deipnosophistai (447c) Ἑλλάνικος δ ἐν Κτίσεσι καὶ ἐκ ῥιζῶν, φησι κατασκευάζεται τὸ βρῦτον γράφων ὧδε πίνουσι δέ βρῦτον ἔκ τινων ῥιζῶν, καθάπερ οἱ Θρᾷκες ἐκ τῶν κριθῶν. Ἑκαταῖος δ ἐν δευτέρῳ Περιηγήσεως εἰπὼν περὶ Αἰγυπτίων ὡς ἀρτοφάγοι εἰσὶν ἐπιφέρει τάς κριθάς ἐς τὸ πῶμα καταλέουσιν. ἐν δέ τῇ τῆς Εὐρώπης περιόδῳ Παίονάς φησι πίνειν βρῦτον ἀπὸ τῶν κριθῶν καὶ παραβίην ἀπὸ κέγχρου καὶ κόνυζαν. ἀλείφονται δέ, φησίν, ἐλαίῳ ἀπὸ γάλακτος. καὶ ταῦτα μέν ταύτῃ. Hellanicus in The Foundings says that beer is made also of rye; he writes as follows: They drink beer made of rye, as the Thracians drink it made of barley. Hecataeus, in the second book of his Description, after saying of the Egyptians that they were bread-eaters, continues: They grind up the barley to make the drink. And in The Description of Europe he says that the Paeonians drink a beer made from barley, also parabias, made from millet, and even fleabane. They also anoint themselves, he says, with an oil made from milk. So much for that. (trans. Gulick) textual fragments = quotations of lost works embedded into other texts Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

70 Why was it reused? They drink beer made of rye, as the Thracians drink it made of barley. the Paeonians drink a beer made from barley, also parabias, made from millet, and even fleabane. They also anoint themselves, he says, with an oil made from milk. Some significance related properties: tf.idf: Except Thracian and Paeonians all other words have a term weight of 0 (function words) or are weak content words. Difference analysis: no discriminating words Log-likelihood ratio: no discriminating words Dale Chall Readability Index: [6.59;9.36] AVG: 7.85 (level of 9th - 10th grade of a secondary school) Is there any measurable content in this fragments? Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors

71 Why was it reused? They drink beer made of rye, as the Thracians drink it made of barley. the Paeonians drink a beer made from barley, also parabias, made from millet, and even fleabane. They also anoint themselves, he says, with an oil made from milk. Dissimilarities in the contextual usage (TLG): (milk,oil): 72% (fleabane, millet): 92%, (parabias, millet): 97%, (fleabane, parabias): 94%, (barley, fleabane): 94%,... (rye, barley): 80% Berti Büchler, Fragmentary Texts and Digital Collections of Fragmentary Authors