Ensuring proper punctuation and letter casing is a key pre-processing step towards applying complex natural language processing algorithms. This is especially significant for textual sources where punctuation and casing are missing, such as the raw output of automatic speech recognition systems. Additionally, short text messages and micro-blogging platforms offer unreliable and often wrong punctuation and casing. This survey offers an overview of both historical and state-of-the-art techniques for restoring punctuation and correcting word casing. Furthermore, current challenges and research directions are highlighted.

LegalNERo is a manually annotated corpus for named entity recognition in the Romanian legal domain. It provides gold annotations for organizations, locations, persons, time and legal resources mentioned in legal documents. Additionally it offers GEONAMES codes for the named entities annotated as location (where a link could be established). The LegalNERo corpus is available in different formats: span-based, token-based and RDF. The Linguistic Linked Open Data (LLOD) version is provided in RDF-Turtle format. CONLLUP files conform to the CoNLL-U Plus format https://universaldependencies.org/ext-format.html . Part-of-speech tagging was realized using UDPIPE. Named entity annotations are placed in the column "RELATE:NE" (the 11th column) as defined in the "global.columns" metadata field. Similarly GEONAMES references are in the column "RELATE:GEONAMES" (the 12th column, last). Automatic processing was performed through the RELATE platform (https://relate.racai.ro). ANN files conform to BRAT format (https://brat.nlplab.org/). The archive contains: - ann_LEGAL_PER_LOC_ORG_TIME_overlap Folder in which all the files are in .ann format and contains annotations of: legal resources mentioned, persons, locations, organizations and time. Overlapping annotations of organizations and time entities inside legal references were allowed. - ann_LEGAL_PER_LOC_ORG_TIME Folder in which all the files are in .ann format and contains annotations of: legal resources mentioned, persons, locations, organizations and time. Overlapping annotations were not allowed and only the longest named entities were annotated. - ann_PER_LOC_ORG_TIME Folder in which all the files are in .ann format and contains annotations of: persons, locations, organizations and time. There are no overlapping annotations. - conllup_LEGAL_PER_LOC_ORG_TIME Folder in which all the files are in .conllup format and contains annotations of: legal resources mentioned, persons, locations, organizations and time. Overlapping annotations were not allowed and only the longest named entities were annotated. The annotation of these files was enhanced with GEONAMES codes (where linking was possible). - conllup_PER_LOC_ORG_TIME Folder in which all the files are in .conllup format and contains annotations of: persons, locations, organizations and time. Overlapping annotations were not allowed and only the longest named entities were annotated. The annotation of these files was enhanced with GEONAMES codes (where linking was possible). - rdf Folder containing the corpus in RDF-Turtle format. All the annotations are available here in both span and token format. - text Folder containing the raw texts. LICENSING This work is provided under the license CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives 4.0 International). The license can be viewed online here: https://creativecommons.org/licenses/by-nc-nd/4.0/ and the full text here: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode . CONTACT Research Institute for Artificial Intelligence "Mihai Draganescu", Romanian Academy Web: http://www.racai.ro Contact emails: vasile@racai.ro , maria@racai.ro

This paper introduces the USPDATRO dataset. This is a speech dataset, in the Romanian language, constructed from open data, focusing on under-represented voice types (children, young and old people, and female voices). The paper covers the methodology behind the dataset construction, specific details regarding the dataset, and evaluation of existing Romanian Automatic Speech Recognition (ASR) systems, with different architectures. Results indicate that more under-represented speech content is needed in the training of ASR systems. Our approach can be extended to other low-resourced languages, as long as open data are available.