O de N et: Compiling a G erman W ord N et from other Resources (2021)

Abstract: The Princeton WordNet for the English language has been used worldwide in NLP projects for many years. With the OMW initiative, wordnets for different languages of the world are being linked via identifiers. The parallel development and linking allows new multilingual application perspectives. The development of a wordnet for the German language is also in this context. To save development time, existing resources were combined and recompiled. The result was then evaluated and improved. In a relatively short time a resource was created that can be used in projects and continuously improved and extended.

## introduction the goal of this initiative is to have a german resource in a multilingual wordnet initiative, where the concepts ( synsets) of the languages are linked, and where the resources are under an open-source license, being included in the nltk language processing package @xcite ) via the wn package @xcite ). wordnet resources are largely used in nlp projects all over the world. our idea is to create a german resource that starts from a crowddeveloped thesaurus; is open; and included in the nltk package. then it can be further developed by researchers while using the resource for their nlp projects. for the first version, we combined existing resources: the openthesaurus german synonym lexicon, 1 the open multilingual wordnet 2 (omw: @xcite the english resource, the princeton wordnet of english (pwn: fellbaum, 1 https://www.openthesaurus.de/ 2 http://compling.hss.ntu.edu.sg/omw/ 1998)). the omw data @xcite was made by matching multiple linked wordnets to wiktionary @xcite and the unicode common locale data repository @xcite . the openthesaurus is a large resource, generated and updated by the crowd. the pwn resource is a well-developed resource for english concepts. it includes many relations between the concepts and is linked to resources for multiple languages. the synsets from the omw data have an estimated accuracy of 90%. we call our new resource "odenet", from "offenes deutsches wordnet -open german wordnet". the first version of odenet was automatically compiled. we also describe the efforts to extend and correct the entries. ## related work in the open multilingual wordnet initiative @xcite , wordnets for several languages were developed and linked. a manually well-designed wordnet resource for german is germanet @xcite . germanet was developed over 20 years now and is very stable and precise. the problem is that it is not under an open-source license and is therefore not broadly used in language technology applications. further, the restricted license makes it impossible to include germanet in the open multilingual wordnet initiative. this is the reason why we decided to build up a new resource. in order not to violate the license terms, we do not use anything from germanet in odenet. vossen (1998, p11) describes two basic approaches to develop new wordnet resources: in the first case (expand), existing pwn synsets are taken and lexical entries added for the specific language. in the second case (merge), languagespecific resources are built and then linked to the pwn. an example of expand is the japanese wordnet @xcite . it is based on translations of pwn to japanese. the japanese wordnet is not fully automatically built: most translations are manually checked. the authors found that there are differences between concept structures in english and japanese, such that several synsets could not be translated. the russian wordnet @xcite is an example of the merge approach. it is based on a monolingual dictionary and the word definitions in these. the idea is that definitions contain hypernyms of the defined words, often in the form of word:hypernym . . . , and that this information can be used to set up hierarchical structures in the wordnet. the approach of the odenet initiative is merge. we use an existing synonym dictionary and try to link the synsets to pwn. @xcite describe the creation of a large thesaurus for russian by means of crowd sourcing. the data is directly collected in a wordnet style, but synsets are not linked to the omw. the basic data for odenet is also generated in a crowd sourcing style, in the openthesaurus project. the openthesaurus project @xcite is a crowd initiative to set up a german synonym lexicon. the version we downloaded in april 2017 has about 120,000 lexical entries in about 36,000 synsets. ## process of creating odenet the first version of odenet was completely automatically created by compilation from openthesaurus. in the following, manual corrections were made in the domains of project management and business reports. german definitions were introduced, relations were corrected and supplemented and cili links (links to the multilingual concepts in omw) were added. then we worked on the syntactic categories. the main focus was on correcting the pos tags of multi-word lexemes.the next step was the annotation of basic german words foot_0 . we annotated all lexical entries (except for function words) of this list with dc:@xmath0 we then added missing entries and corrected synsets manually. then, we implemented an anal-ysis of german nominal compounds and used this information for the addition of hypernym relations. ## current state of odenet the resulting wordnet resource (v1.3) contains about 120,000 lexical entries in about 36,000 synsets. about 20,000 of these synsets are linked to synsets in the english pwn and then to the multilingual cili numbers. there are 2,664 antonym relations and 1,053 pertainym relations linking lexical entities. the number of synset relations can be seen in for evaluation of preciseness, we randomly chose 90 lexical entries, 30 with pos "n", "v" and "a" respectively, and evaluated them manually, see the pos information was correct in 93.3% of the cases. in 5 cases of 6 wrong pos assignments, the lemma was a multi-word lexeme, such as nicht unumstößlich "not unalterable". pos tagging of multi-word lexemes needs more sophisticated procedures than the ones we used here, as standard pos taggers do not tag multi-word expressions. a good part of this problem could be solved with pos corrections in synsets that had lexical items with different pos. the linked english synsets could also give a hint that there might be a problem, as they have pos assigned, which often would be the same for german. a further attempt to improve odenet could therefore be to search for cases where the synsets are linked and the pos tags of the english and german synsets do not match. the german synsets that are linked to english ones, contain the definitions from the correspond-tested correct comment pos 93% many multi-word lexemes definitions 82% in cases of errors, pos of the english words are often different relations 61% in cases of errors, definitions are also wrong we have annotated the entries with a default confidence of 0.6, with entries that have been manually validated given a confidence of 1.0 and those from the extended omw a confidence of 0.85. ## discussion and future plans it has been possible to set up a wordnet for the german language in a couple of years. we have benefited both from openthesaurus and the knowledge in the omw. in this way, we were able to build a very large resource, with the synsets being created manually in the openthesaurus project, and therefore very precise. we have used nlp techniques to add more information, namely pos and the relations to omw and cili. we have used the knowledge in the omw to supplement relations between the german synsets -parallel to the relations in the other wordnets. the open multilingual wordnet initiative is a great chance to get highly linked and standardized language resources for multiple languages. the standardization makes it possible to include these resources in nlp packages, such as nltk or spacy. we have shown that it is possible using nlp techniques to combine language resources such as the openthesaurus and the english pwn to gain a new resource in this standardized multilingual context, with a reasonable precision. the next step will be to further work on the quality of odenet. we have already started to implement methods that allow the semi-manual correction and extension: further, it will again be compared to the english pwn, such that cases where linked synsets differ in their pos assignment will be further investigated. another source of problems is multiword lexemes, where we will have to search for better pos tagging methods. a problem is that the automatic translation method added links of cilis from multiple german synsets, which is undesirable. we need to focus on better linking quality. one approach could be to look at the cases where the pos of german and english differs. we started to work on the basic german words, adding and correcting information. this will be a valuable information source for simplified language projects. through the wn library (goodman and bond, 2021) the resource will be available to nltk, such that it can be used in nlp projects. the opensource idea will help to let researchers working on german language further improve and expand odenet. we ourselves plan to use it in information extraction in the business domain and sentiment analysis projects. by this approach, we will add synsets from the business domain and sentiment polarity for many words. we will add a user interface to make crowd development possible, in order to extend and correct odenet. we would also like to tag some german texts. the resource is available on github under an open-source license: https://github.com/ hdasprachtechnologie/odenet .