A WordNet in GF

This is an attempt to port the Princeton WordNet to GF. In parallel with the pure English WordNet we also build a version for Swedish and Bulgarian. WordNets for all other languages are bootstrapped from existing resources and aligned by using statistical methods. For details check:

• Krasimir Angelov. A Parallel WordNet for English, Swedish and Bulgarian. LREC 2020
• Krasimir Angelov, Gleb Lobanov. Predicting Translation Equivalents in Linked WordNets. COLING 2016.

Unlike the original WordNet we focus on grammatical, morphological as well as semantic features. In addition, the WordNets for different languages are aligned on the lexeme level, rather than on the synset level. All this is necessary to make the lexicon compatible with the Resource Grammars Library (RGL).

The Lexicon

Each entry in the lexicon represents the full morphology, precise syntactic category as well as one particular sense of a word. If three words in three different languages share the same meaning then they are represented as a single cross lingual id. For example in WordNetEng.gf we have all those definitions of blue in English:

lin blue_1_A = mkA "blue" "bluer";
lin blue_2_A = mkA "blue" "bluer";
lin blue_3_A = mkA "blue" "bluer";
lin blue_4_A = mkA "blue" "bluer";
lin blue_5_A = mkA "blue" "bluer";
lin blue_6_A = mkA "blue" "bluer";
lin blue_7_A = mkA "blue" "bluer";
lin blue_8_A = mkA "blue" "bluer";

since they represent different senses and thus different translations in WordNetSwe.gf and WordNetBul.gf:

lin blue_1_A = L.blue_A ;
lin blue_2_A = L.blue_A ; --guessed
lin blue_3_A = mkA "deppig" ;
lin blue_4_A = mkA "vulgär" ;
lin blue_5_A = mkA "pornografisk" ;
lin blue_6_A = mkA "aristokratisk" ;
lin blue_7_A = L.blue_A ; --guessed
lin blue_8_A = L.blue_A ; --guessed

lin blue_1_A = mkA086 "син" ;
lin blue_2_A = mkA086 "син" ; --guessed
lin blue_3_A = mkA076 "потиснат" ;
lin blue_4_A = mkA079 "вулгарен" ;
lin blue_5_A = mkA078 "порнографски" ;
lin blue_6_A = mkA079 "аристократичен" ;
lin blue_7_A = mkA086 "син" ; --guessed
lin blue_8_A = mkA086 "син" ; --guessed

The definitions are using the standard RGL syntactic categories which are a lot more descriptive than the tags ´n´, ´v´, ´a´, and ´r´ in the WordNet. In addition we use the RGL paradigms to implement the morphology.

Note also that not all translations are equally reliable for all languages. In the example above, the comment --guessed means that the translation was extracted from an existing translation lexicon, but we are not sure if it accurately represents the right sense. Similarly sometimes you can also see the comment --unchecked, which means that the chosen translation comes from an existing WordNet but still further checking is needed to guarantee that this is the most idiomatic translation.

The English lexicon contains also information for the gender. All senses that refer to a human being are tagged with either ´masculine´, ´feminine´ or ´human´ gender. In some cases where the word is either masculine or feminine then it is further split into two senses. In those cases there is usually a different translation in Bulgarian. The information about which words refer to humans is based on the WordNet hierarchy. In the English RGL the gender information is relevant, for instance when choosing between who/which and herself/himself/itself.

In the abstract syntax WordNet.gf are defined all abstract ids in the lexicon. Almost all definitions are also followed by a comment which consists of, first the corresponding WordNet 3.1 synset offset, followed by dash and then the wordnet tag. After that there is a tab followed by the WordNet gloss. Like in WordNet the gloss is followed by a list of examples, but here we retain only the examples relevant to the current lexical id. In other words, if a synset contains several words, only the examples including the current abstract id are retained.

The verbs in the lexicon are also distinguished based on their valency, i.e. transitive, intransitive, ditransitive, etc. The valency of a verb in a given sense is determined by its example, but there is also a still partial integration of VerbNet.

Some of the nouns and the adjectives are typically accompanied by prepositions and a noun phrase. In those cases they are classified as N2 and A2. This helps in parsing and also let us to choose the right preposition in every language.

WordNet Domains

The data also integrates WordNet Domains. If the synset for the current entry has domain(s) in WordNet Domains, then they are listed in the abstract syntax, at the beginning of the gloss, surrounded by square brackets. In addition to those, some more domains are added by analysing the glosses in the original WordNet. The taxonomy of the domains is stored in domains.txt

Treebank

In order to make the lexical development more stable we have also started a treebank consisting of all examples from the Princeton WordNet (see examples.txt). The examples are automatically parsed with the Parse.gf grammar in GF. For each example there is also the original English sentence, as well as the Swedish and Bulgarian seed translations from Google Translate.

Some of the examples are already checked. This means that the abstract syntax tree is corrected, the right senses are used and the seed translations are replaced with the translations obtained by using the Parse grammar. The translations are also manually checked for validity.

The format of a treebank entry is as follows:

abs: PhrUtt NoPConj (UttS (UseCl (TTAnt TPast ASimul) PPos (PredVP (DetCN (DetQuant DefArt NumSg) (UseN storm_1_N)) (UseV abate_2_V)))) NoVoc
eng: The storm abated
swe: stormen avtog
bul: бурята отслабва
key: 1 abate_2_V 00245945-v

If the first line starts with "abs*" instead of "abs:" then the entry is not checked yet. If the line starts with "abs^" then the entry is checked by some more work is needed.

The last line in the entry contains the abstract ids for which this example is intended. The number 1 here means that there is only one id but there could be more if they share examples. After the abstract ids is the synset offset in WordNet 3.1. If the same synset has translations in either BulTreebank WordNet or Svenskt OrdNät then they are also listed after the offset. When possible these translations should be used.

VerbNet

VerbNet is invaluable in ensuring that the verb valencies are as consistent as possible. The VerbNet also gives us the semantics of the verbs as a first-order logical formula.

The VerbNet classes and frames are stored in examples.txt. For example the following record encodes class begin-55.1.

class: begin-55.1
role:  Agent +animate +organization
role:  Theme
role:  Instrument

After the class definition, there are one or more frame definitions:

frm: PredVP Agent (ComplVV Verb ? ? Theme)
sem: begin(E,Theme), cause(Agent,E)
key: go_on_VV proceed_VV begin_to_1_VV start_to_1_VV commence_to_1_VV resume_to_1_VV undertake_1_VV get_34_VV set_about_3_VV set_out_1_VV get_down_to_VV

The line marked with frm: represents the syntax of the frame expressed in the abstract syntax. After that sem: contains the first-order formula. Finally key: lists all abstract syntax entries belonging to that frame.

After a frame, there might be one or more examples which illustrates how the frame is applied to different members of the frame.

Wikipedia Images

About 1/5 of the lexical entries in the lexicon are linked to the corresponding Wikipedia articles. Together with the link to the article we also store the path to the thumbnail image in the page. The links are stored in images.txt. The format is as follows:

gothenburg_1_PN	11861;Gothenburg;commons/0/02/Gothenburg_new_montage_2015-2.png

This is a space separated record. The value in the first field is the abstract lexical id, which is followed by one field per image. The image field consists of three parts separated by semicolumns. The first part is the page id in the Wikipedia database, the second is the relative page location, and the last one is the relative path to the thumbnail image.

Browsing and Editing

An online search interface for the lexicon is available here:

https://cloud.grammaticalframework.org/wordnet/

Via the interface it is also possible to log in with your GitHub account and edit the data. Both the lexicon and the corpus are editable. Once you finish a batch of changes you can also commit directly to the repository.