ParlaMint II: The Show Must Go On
In ParlaMint I, a CLARIN-ERIC supported project in pandemic times, a set of comparable and uniformly annotated multilingual corpora for 17 national parliaments were developed and released in 2021. For 2022 and 2023, the project has been extended to ParlaMint II, again with the CLARIN ERIC financial support, in order to enhance the existing corpora with new data and metadata; upgrade the XML schema; add corpora for 10 new parliaments; provide more application scenarios and carry out additional experiments. The paper reports on these planned steps, including some that have already been taken, and outlines future plans.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Adding the Basque Parliament Corpus to ParlaMint Project
The aim of this work is to describe the colection created with transcript of the Basque parliamentary speeches. This corpus follows the constraints of the ParlaMint project. The Basque ParlaMint corpus consists of two ve…
Entity Linking in the ParlaMint Corpus
The ParlaMint corpus is a multilingual corpus consisting of the parliamentary debates of seventeen European countries over a span of roughly five years. The automatically annotated versions of these corpora provide us wi…
Entity LinkingMaking Italian Parliamentary Records Machine-Actionable: the Construction of the ParlaMint-IT corpus
This paper describes the process of acquisition, cleaning, interpretation, coding and linguistic annotation of a collection of parliamentary debates from the Senate of the Italian Republic covering the COVID-19 period an…
POSParlaMint-RO: Chamber of the Eternal Future
The present paper aims to describe the collection of ParlaMint-RO corpus and to analyse several trends in parliamentary debates (plenary sessions of the Lower House) held in between 2000 and 2020). After a short descript…
The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings
Recent significant improvements in speech and language technologies come both from self-supervised approaches over raw language data as well as various types of explicit supervision. To ensure high-quality processing of …