Lexical data API

This API provides data from various dictionary resources of K Dictionaries across 50 languages. It is used by language service providers, app developers, and researchers, and returns data as JSON documents. A basic search result consists of an object containing partial lexical information on entries that match the search criteria, but further in-depth information is also available. Basic search parameters include the source resource, source language, and text (lemma), and the entries are returned as objects within the results array. It is possible to look for words with specific syntactic criteria, specifying the part of speech, grammatical number, gender and subcategorization, monosemous or polysemous entries. When searching by parameters, each entry result contains a unique entry ID, and each sense has its own unique sense ID. Using these IDs, it is possible to obtain more data – such as syntactic and semantic information, multiword expressions, examples of usage, translations, etc. – of a single entry or sense. The software demonstration includes a brief overview of the API with practical examples of its operation

A multilingual evaluation dataset for monolingual word sense alignment

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.The authors would like to thank the three anonymous reviewers for their insightful suggestions and careful reading of the manuscript. This work has received funding from the EU’s Horizon 2020 Research and Innovation programme through the ELEXIS project under grant agreement No. 731015. The contributions in Bulgarian were partially funded by the Bulgarian National Interdisciplinary Research e-Infrastructure for Resources and Technologies in favor of the Bulgarian Language and Cultural Heritage, part of the EU infrastructures CLARIN and DARIAH – CLaDA-BG, Grant number DO1- 272/16.12.2019. This work is also supported by Sci- ence Foundation Ireland (SFI) under the Insight Center for Data Analytics (Grant Number SFI/12/RC/2289) and the Irish Research Council under the “Cardamom” Consolidator Laureate Grant (IRCLA/2017/129).peer-reviewed2020-05-1