#BABELNET THESAURUS CODE#

Hence the two subsequent phases, which, together with the final resource, represent the novel contribution of this paper.ĭisambiguation of related terms: we fully disambiguate all lexical information associated with a proto-concept (i.e. This is a distributionally-based conceptualization with some degree of taxonomic information only. senses), their similar and related terms, as well as aggregated contextual clues per proto-concept (Table 1a). The resulting structure contains disambiguated proto-concepts (i.e. Learning a JoBimText model: initially, we automatically create a sense inventory from a large text collection using the pipeline of the JoBimText project Footnote 1. This results in a new knowledge resource that we refer to as a hybrid aligned resource. To this end, we develop a methodology to automatically induce distributionally-based semantic representations from large amounts of text, and link them to a reference knowledge base. Far from being technicalities, such properties are crucial to be able to embed a resource of this kind into the Semantic Web ecosystem, where human-readable distributional representations are explicitly linked to URIfied semantic resources. In this paper, we follow this line of research and take it one step forward by producing a hybrid knowledge resource, which combines symbolic and statistical meaning representations while (i) staying purely on the lexical-symbolic level, (ii) explicitly distinguishing word senses, and (iii) being human readable. These include approaches that modify Word2Vec to learn sense embeddings, methods to enrich WordNet with embeddings for synsets and lexemes, acquire continuous word representations by combining random walks over knowledge bases and neural language models, or produce joint lexical and semantic vectors for sense representation from text and knowledge bases. Complementary to this, a plethora of works in Natural Language Processing (NLP) has recently focused on combining knowledge bases with distributional information from text. To learn more, see the privacy policy.Recent years have witnessed an impressive amount of work on the automatic construction of wide-coverage knowledge resources from Wikipedia and the Web. Please note that Urban Thesaurus uses third party scripts (such as Google Analytics and advertisements) which use cookies.

#BABELNET THESAURUS CODE#

Special thanks to the contributors of the open-source code that was used in this project: and you might like to check out the growing collection of curated slang words for different topics over at Slangpedia. There is still lots of work to be done to get this slang thesaurus to give consistently good results, but I think it's at the stage where it could be useful to people, which is why I released it. Please also note that due to the nature of the internet (and especially UD), there will often be many terrible and offensive terms in the results. Or you might try boyfriend or girlfriend to get words that can mean either one of these (e.g. The search algorithm handles phrases and strings of words quite well, so for example if you want words that are related to lol and rofl you can type in lol rofl and it should give you a pile of related slang terms. The higher the terms are in the list, the more likely that they're relevant to the word or phrase that you searched for. Note that this thesaurus is not in any way affiliated with Urban Dictionary.ĭue to the way the algorithm works, the thesaurus gives you mostly related slang words, rather than exact synonyms. The official Urban Dictionary API is used to show the hover-definitions. These indexes are then used to find usage correlations between slang terms. The Urban Thesaurus was created by indexing millions of different slang terms which are defined on sites like Urban Dictionary.

Babelnet thesaurus