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Dataset Card for Dataset Name
Parallel basic locative constructions in English and 26 target languages.
Dataset Details
Dataset Description
We introduce ParaBLoCC, the Parallel Basic Locative Construction Corpus, the first multilingual compendium of this important grammatico-functional construction, and par- ticularly the first such corpus containing semantically equivalent BLCs in source/target language pairs. The data —taken from bitext corpora in English paired with twenty-six ty- pologically diverse languages —are likely to prove useful for studying questions of cogni- tive underpinnings and cross-linguistic usage patterns of spatial expressions, as well as for improving multilingual spatial relation extraction and related tasks.
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- Language(s) (NLP): en, sw, fi, hu, ca, cs, nl, fr, de, el, it, pl, ru, es, sv, ig, ja, ko, ay, qu, am, ar, he, ti, zh, tr, uz
- License: MIT (Derived from Opus Portal (Tiedeman et al 2022))
Dataset Sources [optional]
- Repository: [https://github.com/pviechnicki/parablocc]
- Paper [optional]: [https://aclanthology.org/2024.eacl-long.66/]
- Demo [optional]: NA
Uses
We created the ParaBLoCC data to appeal to a wide variety of scholars interested in spatial lan- guage, and by making them available we hope to encourage additional study in this area. The pri- mary utility of the data are to allow study of usage patterns for parallel spatial expressions in twenty- six genetically and typologically diverse languages. Through automated alignment and span detection, silver labels for BLCs in the target languages can be extracted and studied themselves or used for downstream tasks. Likely secondary uses for the ParaBLoCC data will be to enable work on multilingual aspects of spatial relation extraction (Rawsthorne et al., 2023). Until very recently, text corpora annotated for spa- tial relation triples were limited to the most high- resource numbers of languages, though this situa- tion is starting to improve (Wang et al., 2023) so the multilinguality of ParaBLoCC should be wel- come. The data can be used to improve current models of geospatial expression resolution (Wang et al., 2024). Finally we expect multilingual image caption models (Ramos et al., 2023) will benefit from the parallel data collected by ParaBLoCC.
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Dataset Structure
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Dataset Creation
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Source Data
Data Collection and Processing
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Annotation process
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Personal and Sensitive Information
To our knowledge, the dataset contains no PII or sensitive information.
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Bias, Risks, and Limitations
The datasets are drawn from particular domains, such as news, subtitles, parliamentary speeches, religious texts, etc, and share the same biases with those domains.
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Dataset Card Contact
Peter Viechnicki, [email protected]
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