HDT-UD, the largest German UD treebank by a large margin, as well as the German-LIT treebank, currently do not analyze preposition-determiner contractions such as zum (= zu dem, “to the”) as multi-word tokens, which is inconsistent both with UD guidelines as well as other German UD corpora (GSD and PUD). In this paper, we show that harmonizing corpora with regard to this highly frequent phenomenon using a lookup-table based approach leads to a considerable increase in automatic parsing performance

Friedrich, Annemarie

Grünewald, Stefan

English

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Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020), pages 94–98Barcelona, Spain (Online), December 13, 202094Unifying the Treatment of Preposition-Determiner Contractionsin German Universal Dependencies TreebanksStefan Grünewald1,2 Annemarie Friedrich21Institut für Maschinelle Sprachverarbeitung, University of Stuttgart2Bosch Center for Artificial Intelligence, Renningen, Germanystefan.gruenewald|annemarie.friedrich@de.bosch.comAbstractHDT-UD, the largest German UD treebank, as well as the German-LIT treebank, currently donot analyze preposition-determiner contractions such as zum (= zu dem, “to the”) as multi-wordtokens, which is inconsistent both with UD guidelines as well as other German UD corpora(GSD and PUD). In this paper, we show that harmonizing corpora with regard to this highlyfrequent phenomenon using a lookup-table leads to a considerable increase in automatic parsingperformance.1 IntroductionUniversal Dependencies (UD) are a cross-linguistic dependency grammar framework driven by a large-scale multi-lingual community effort (de Marneffe et al., 2014). In general, UD prioritizes relationsbetween content words. The treatment of function words, being a rather language-specific issue, is todate sometimes inconsistent even across the treebanks of a single language. Function words includingprepositions or negation words are often contracted with other words, which requires to decide whetherto keep them as a fused unit or split them up during tokenization – a non-trivial problem.1The German language allows to fuse certain combinations of preposition+determiner into a singletoken, resulting in what UD refers to as a multiword token, i.e., a single token that contains more thanone syntactic word. Examples include zum (= zu dem, “to the”) and ins (= in das, “into the”). Theseconstructions can even be regarded as lexicalized, i.e., as belonging to the inventory of the language’slexicon (Lehmann, 2002). Expanding such contractions into several tokens does not depend on thecontext, hence, treating them as multi-word tokens is rather straightforward. The only caveat is assigningthe correct morphological features to the determiner, but these can easily be retrieved from the head noun.The current UD annotation guidelines for German2 suggest treating preposition-determiner contrac-tions as multi-word tokens in the way outlined above, and their treatment is implemented accordingly inthe German GSD treebank (292k tokens) as well as in German PUD (21k tokens). However, HDT-UD(Borges Völker et al., 2019), the largest German UD treebank (with 190k sentences and 3.4 million to-kens currently the largest available UD treebank overall), as well as the small German-LIT treebank (40ktokens, Salomoni (2017)), do not expand these tokens. This may lead to inconsistency-based parsingerrors in cross-treebank experiments or when training a parser on several treebanks.In this paper, we analyze the extent of the problem, finding that in HDT-UD, 25% of all sentencescontain such contractions. Our contributions are (i) the development of a simple lookup-based script forsplitting German preposition-determiner contractions into several tokens, and (ii) a set of experimentsshowing that on the relevant sentences, the increased consistency leads to an increase in parsing accuracyby up to 0.8 points in terms of LAS F1. Hence, this paper constitutes a case study of the improvements wecan expect from careful linguistic data analysis and corpus harmonization. We contribute our conversionscript, as well as the fixed versions of the HDT-UD and German-LIT corpora, for the next UD release.This work is licensed under a Creative Commons Attribution 4.0 International License. License details:http://creativecommons.org/licenses/by/4.0/.1See, e.g., https://github.com/UniversalDependencies/docs/issues/6412https://universaldependencies.org/de/index.html95Contraction Expansion count % sentsim in dem 26236 12.8am an dem 7764 3.9zum zu dem 7584 3.9zur zu der 6149 3.1vom von dem 3404 1.8beim bei dem 2795 1.4ins in das 1422 0.7fürs für das 233 0.1ans an das 160 0.1übers über das 147 0.1TOTAL 56150 25.0(a) German-HDT-UDContraction Expansion count % sentsim in dem 89 4.2zur zu der 44 2.0zum zu dem 27 1.4vom von dem 17 0.9am an dem 17 0.9ins in das 8 0.4aufs auf das 7 0.3beim bei dem 5 0.3fürs für das 5 0.3beym bey dem 3 0.1TOTAL 222 9.8(b) German-LITTable 1: The 10 most common contractions in two German UD corpora, as well as the total count. (Thecomputation of the TOTAL row considers the fact that one sentence may contain several contractions.)2 Preposition–Determiner Contractions in German UD corporaThe German HDT-UD treebank (Borges Völker et al., 2019) has been automatically converted from theHamburg Dependency Treebank (HDT, Foth et al. (2014)) using a tree transducer. The text data stemsfrom the German technical website heise.de, which contains, among others, reports about new softwareand hardware as well as technology-related politics. HDT uses its own dependency annotation scheme(Foth, 2006), in which, in contrast to UD, relations are headed by function words. Preposition-determinercontractions are simply marked as prepositions and indicate their complement (i.e., what would be apreposition’s head noun in UD) using the relation PN.Table 1(a) reports corpus statistics for the occurrences of the most frequent preposition-determinercontractions in HDT-UD. Contractions occur in 25% of all sentences, showing that treating the phe-nomenon in a consistent way is non-negligible. Some contractions, including im (= in dem, “in the”), am(= an dem, “at the”) and zum (= zu dem, “to the”) are extremely common; others are more colloquial andrarer in written German (e.g., übers = über das, “over the”).APPR ART ADJA NNADP DET ADJ NOUNim [in dem] letzten Jahrcasedetamod(a) Expanded contraction (according to guidelines).APPRART ADJA NNADP ADJ NOUNim letzten Jahrcaseamod(b) HDT-UD / German-LIT original version.Figure 1: Annotation of contractions in the phrase im letzten Jahr (“in the last year”).Figure 1 illustrates the two different ways of treating preposition-determiner contractions in GermanUD corpora. Version (a), introducing two trace-like tokens, is suggested by the official guidelines, andapplied in GSD and German-PUD. The commonly used Stanza tokenizer (Qi et al., 2020) also employsthis strategy. HDT-UD currently applies a single-token analysis (b). The contractions have their ownlanguage-specific XPOS tag APPRART (“preposition with article”) taken from the STTS tagset (Schilleret al., 1995). Their UPOS tag in HDT is ADP, which further shows that the single-token analysis is inad-equate: ADP stands for adpositions (a cover term for prepositions and postpositions), but the contractionsalso include a determiner in addition to a preposition. This information is lost in the single-token analy-sis, which attaches the contraction to its head via case, circumventing det altogether.The same issue exists in the small German-LIT treebank (Salomoni, 2017), which consists of shortfragments of 18th century literary essays about aesthetical issues by Schlegel and Novalis, written in thethen-young Hochdeutsch (modern German). Corpus statistics for German-LIT are given in Table 1(b).96For each token with index i: if the token has the APPRART XPOS tag(and no exception applies*):1. Increase the indices of all tokens after index i by 1.2. Insert the contraction as a new multiword token with index (i, i+1)3. Replace the contraction at i with two new tokens: a preposition anda determiner as specified by the lookup table.4. Attach the preposition and determiner to the contractions’s head viathe case and det relations, respectively.5. Copy the contraction’s head’s Case feature to the preposition and itsCase, Number, Gender features to the determiner.*Exceptions include tokens that are clearly incorrectly tagged and tokensattached via the reparandum relation (i.e., disfluencies).1-2 Im _ _ _ _1 In ADP APPR 3 case2 dem DET ART 4 det3 letzten ADJ ADJA 3 amod4 Jahr NOUN NN 4 obl5 stieg VERB VVFIN 0 root6 der DET ART 6 det7 Umsatz NOUN NN 4 nsubjExample CoNLL-U file (output of algorithm).“In the last year increased the sales”Figure 2: Algorithm for lookup-table based preposition-determiner expansion in German UD.3 Expanding ContractionsBased on the above discussion, we propose a simple lookup-based method for expanding preposition-determiner contractions in German UD corpora into multi-word tokens in order to make the data con-sistent with the UD annotation guidelines. Figure 2 shows our algorithm, which operates on files of theCoNLL-U format.3 Note that we do not make changes for the token z. from z. B. = zum Beispiel = “forexample,” as well as in several other infrequent special cases resulting from annotation errors.Since prepositions and determiners are closed word classes and the mapping from contractions to theirexpansions is unambiguous in German, we use a simple lookup table (see Table 1) for expansion. The fulltable was constructed by extracting all word forms labeled APPRART from the HDT-UD and LIT corporaand then using the authors’ knowledge of German to assign the correct expansions. Morphologicalfeatures of the syntactic words of the expansion may be ambiguous: for example, zum may be expandedinto zu demGender=Neut or zu demGender=Masc. However, we can easily derive the correct features by simplycopying over the Case, Number, Gender features from the syntactic head of the contraction. (Note,however, that potential annotation errors are also propagated this way.)We ensure the correctness of the output by manually inspecting some of the resulting annotated sen-tences as well as running the official UD validation script.44 Parser EvaluationTo evaluate how our changes to the corpora affect parsing accuracy, we train the state-of-the-art UDifyparser (Kondratyuk and Straka, 2019) on the existing GSD corpus as well as the original and modifiedversions of the HDT-UD corpus, and report scores on various versions of the test sets. (LIT and PUDonly provide test data.) We keep all hyperparameters the same as in the original setup, except that (a) weuse the German BERT model by deepset5 to initialize BERT weights; and (b) we only train on HDT-UDfor 25 epochs due to the extremely large size of the corpus. We report results for gold tokens.Table 2 shows parser performance on sentences containing contractions in the test sets of GSD, HDT-UD, and LIT (the latter two in both their original and modified versions, indicated by +/-exp). As can beseen, for the parser that is trained on the GSD corpus (in which contractions are split up), performance ishigher on the modified versions of the HDT and LIT corpora than on the original versions, as would beexpected due to the unified treatment of contractions. Interestingly, the same holds when training on theoriginal version of HDT-UD. The reason for this is that the original corpus also contains many cases ofnon-contracted preposition-determiner combinations, enabling the parser to get expanded contractionsright as well. Furthermore, scores increase on the fixed test sets because the expansion of contractionsleads to more det relations, which are generally very easy to predict. However, training on the fixed datais still beneficial, as comparing the last two rows for each of the +exp test sets shows.3https://universaldependencies.org/format.html4https://github.com/UniversalDependencies/tools/blob/master/validate.py5https://deepset.ai/german-bert97test↓ train GSD+exp PUD+exp HDT-exp HDT+exp LIT-exp LIT+expGSD+exp 85.78 85.34 85.70 86.50 79.76 80.45HDT-exp 79.15 81.32 95.57 95.72 76.33 76.91HDT+exp 79.23 81.41 95.45 95.73 76.34 77.11Table 2: Parsing performance (LAS F1) on test set sentences that contain contractions. +exp indicatesthat contractions are analysed as multi-word tokens in the respective corpus, -exp indicates that they areanalysed as single tokens.An analysis by label type confirms that increases in accuracy are mainly caused by case and det. Forexample, when training on GSD, LAS F1 of case increases from 96.28 (HDT-exp) to 96.60 (HDT+exp);det increases from 96.74 (HDT-exp) to 97.72 (HDT+exp). We also observe modest improvements inparsing accuracy on a number of other dependency labels such as obl, nmod, and ccomp, indicating thatsurrounding syntactic constructions also benefit from the consistent handling of contractions.Interestingly, our results also show that the parser trained on GSD, despite not having encountered con-tractions during training, still attaches the vast majority of contractions in HDTorig/LITorig correctly. Wesuspect that this may be owed to BERT’s ability to generalize from simple prepositions to contractionsbecause of their similar distribution in the pre-training data.5 Related WorkDiscussions within UD community. It is an on-going discussion within the UD community how to bestachieve a standardized treatment of tokenization and word tokenization, e.g., how to treat multi-wordtokens such as gonna (= going to) in English. A current proposal6 suggests breaking up these tokens forformal English as was done in the original Penn TreeBank annotations, but allowing different solutionsfor informal language such as Twitter posts. We follow this suggestion in some sense as we also createmulti-word tokens for highly frequent and lexicalized preposition-determiner contractions in German.Within the context of Surface-Syntactic Universal Dependencies (SUD, Gerdes et al. (2018)), it hasbeen proposed to treat propositions as heads, moving away from UD’s focus on content words andapplying distributional criteria for the units instead. In fact, in SUD, the question of how to deal withthese contractions is even more pressing: Because the preposition part of the contraction would be thehead of a noun, but the determiner part would be a dependent, it is not quite clear what the overallsyntactic relation of the contraction to the noun should be if left as one syntactic word.French amalgames. A similar issue arises in the word segmentation of French amalgames (contrac-tions). Here, the situation is slightly more complicated due to the ambiguity of du/des, which may occureither as indefinite determiners as in des enfants jouent (“(some) kids play”), in partitive constructionssuch as je bois du lait (= de le, “I drink (some) milk”), or in possessive constructions such as la lettre desfilles (de les, “the letter of the girls”). Currently, the major French treebanks annotate infinite determinerdes as DET, while they split the other two cases into two tokens. However, a context-dependent treat-ment of tokenization bears technical difficulties for automatic processing; a discussion by the treebankmaintainers suggests to always split these contractions and annotate the indefinite determiner case usinga fixed relation between the two components.7 To date, this does not seem to have been implemented.6 ConclusionIn this paper, we have carefully analysed the treatment of preposition-determiner contractions in GermanUD corpora. Harmonizing representations lead to increases in LAS F1 of up to 0.8, indicating thata unified treatment of these frequent construction is essential. We here have presented a case studyof how to unify word segmentation for the relatively simple case of German preposition-determinercontractions. Future work includes addressing similar phenomena in more difficult situations such as6https://github.com/UniversalDependencies/docs/issues/6417https://github.com/bguil/UD-French-discussion/issues/198the context-dependent interpretation of French amalgames or, more generally, finding good guidelinesfor word segmentation and harmonizing corpora accordingly. The latter is especially tricky for informalgenres where segmentation decisions seem to be a continuum.ReferencesEmanuel Borges Völker, Maximilian Wendt, Felix Hennig, and Arne Köhn. 2019. HDT-UD: A very large uni-versal dependencies treebank for German. In Proceedings of the Third Workshop on Universal Dependencies(UDW, SyntaxFest 2019), pages 46–57, Paris, France, August. Association for Computational Linguistics.Marie-Catherine de Marneffe, Timothy Dozat, Natalia Silveira, Katri Haverinen, Filip Ginter, Joakim Nivre, andChristopher D. Manning. 2014. Universal Stanford dependencies: A cross-linguistic typology. In Proceedingsof the Ninth International Conference on Language Resources and Evaluation (LREC’14), pages 4585–4592,Reykjavik, Iceland, May. European Language Resources Association (ELRA).Kilian A. Foth, Arne Köhn, Niels Beuck, and Wolfgang Menzel. 2014. Because size does matter: The ham-burg dependency treebank. In Proceedings of the Ninth International Conference on Language Resources andEvaluation (LREC’14), pages 2326–2333, Reykjavik, Iceland, May. European Language Resources Association(ELRA).Foth, 2006. Eine umfassende Constraint-Dependenz-Grammatik des Deutschen.Kim Gerdes, Bruno Guillaume, Sylvain Kahane, and Guy Perrier. 2018. SUD or surface-syntactic universaldependencies: An annotation scheme near-isomorphic to UD. In Proceedings of the Second Workshop on Uni-versal Dependencies (UDW 2018), pages 66–74, Brussels, Belgium, November. Association for ComputationalLinguistics.Dan Kondratyuk and Milan Straka. 2019. 75 languages, 1 model: Parsing universal dependencies universally.In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9thInternational Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2779–2795, HongKong, China, November. Association for Computational Linguistics.Christian Lehmann, 2002. New reflections on grammaticalization and lexicalization, volume 49, pages 1–18. 01.Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. Stanza: A Python naturallanguage processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of theAssociation for Computational Linguistics: System Demonstrations.Alessio Salomoni. 2017. Toward a treebank collecting german aesthetic writings of the late 18th century. InProceedings of the Fourth Italian Conference on Computational Linguistics (CLiC-it).Anne Schiller, Simone Teufel, and Christine Thielen. 1995. Guidelines für das tagging deutscher textcorpora mitstts. Universität Stuttgart, Universität Tübingen, Germany.

Unifying the treatment of preposition-determiner contractions in German universal dependencies treebanks

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Unifying the treatment of preposition-determiner contractions in German universal dependencies treebanks

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