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Lexical data API
(2022)
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.
Wortgeschichte digital (Digital Word History) is an emerging historical dictionary of the German language that focuses on describing semantic shifts from about 1600 through today. This article provides deeper insight into the dictionary’s “cross-reference clusters,” one of its software tools that performs visualization of its reference network. Hence, the clusters are a part of the project’s macrostructure. They serve as both a means for users to find entries of interest and a tool to elucidate relations among dictionary entries. Rather than delve into technical aspects, this article focuses on the applied logics of the software and discusses the approach in light of the dictionary’s microstructure. The article concludes with some considerations about the clusters’ advantages and limitations.
Brown clustering has been used to help increase parsing performance for morphologically rich languages. However, much of the work has focused on using clustering techniques to replace terminal nodes or as a feature for parsing. Instead, we choose to examine how effectively Brown clustering is for unlexicalized parsing by creating data-driven POS tagsets which are then used with the Berkeley parser. We investigate cluster sizes as well as on what information (e.g. words vs. lemmas) clustering will yield the best parser performance. Our results approach the current state of the art results for the German T¨uBa-D/Z treebank when using parser internal tagging.