Data collection has been done by the means of Sketch Engine program.
Data were extrapolated from the annotated English web corpus enTenTen20.
Data collection and analysis has been done during the period of two months: April and May 2023.
Recently, the enTenTen20 corpus has been updated to a newer version - enTenTen21. Nevertheless, the older version is still available, can be worked on and can be compared with the newer one. It has been noticed that the differences between the two versions of the English web corpus did not affect the results of this study. The only apparent difference was seen in slightly different numbers in frequency values for specific collocations. This was expected since the older version of web corpus consists of 36 billion words, while the new version counts 52 billion words. On the other hand, as noted above, these frequency deviations were not significant enough to refute the hypotheses. They have rather confirmed them once again.
This study is one of the results of work on a larger scientific-research project called "Metaphorical collocations - syntagmatic relations between semantics and pragmatics". More information about the project is available on the following link: https://metakol.uniri.hr/en/opis-projekta/
The study has been financed by the Croatian science foundation.
Working with the data/replicating the study:
Data collected for the purposes of this study is available in CSV format.
Data for each gustatory adjective (collocate) is presented in a separate CSV file.
Upon opening each file, stretch the borders of every column for better visibility of data.
Tables show different collocational bases (nouns) which are found in the corpus, in combination with a specific gustatory adjective, their collocate.
These nouns are listed by their score number (The Mutual Information score expresses the extent to which words co-occur compared to the number of times they appear separately).
Tables show what type of mapping is present in a certain collocation (e.g., intra-modal or cross-modal).
Tables show what type of meaning or cognitive process is working in the background of the meaning formation (e.g., metonymic or metaphoric).
For every analyzed collocation, we provided a contextualized example of its use from the corpus, along with the hyperlink where it can be found.
The corpus contains recordings by the native speakers of the North Levantine Arabic (apc) acquired during 2020, 2021, and 2023 in Prague, Paris, Kabardia, and St. Petersburg. Altogether, there were 13 speakers (9 male and 4 female, aged 1x 15-20, 7x 20-30, 4x 30-40, and 1x 40-50).
The recordings contain both monologues and dialogues on the topics of everyday life (health, education, family life, sports, culture) as well as information on both host countries (living abroad) and country of origin (Syria traditions, education system, etc.). Both types are spontaneous, the participants were given only the general subject and talked on the topic or discussed it freely. The transcription and translation team consisted of students of Arabic at Charles University, with an additional quality check provided by the native speakers of the dialect.
The textual data is split between the (parallel) transcriptions (.apc) and translations (.eng), with one segment per line. The additional .yaml file provides mapping to the corresponding audio file (with the duration and offset in the "%S.%03d" format, i.e., seconds and milliseconds) and a unique speaker ID.
The audio data is shared in the 48kHz .wav format, with dialogues and monologues in separate folders. All of the recordings are mono, with a single channel. For dialogues, there is a separate file for each speaker, e.g., "Tar_13052022_Czechia-01.wav" and "Tar_13052022_Czechia-02.wav".
The data provided in this repository corresponds to the validation split of the dialectal Arabic to English shared task hosted at the 21st edition of the International Conference on Spoken Language Translation, i.e., IWSLT 2024.
The corpus contains recordings by the native speakers of the North Levantine Arabic (apc) acquired during 2020, 2021, and 2023 in Prague, Paris, Kabardia, and St. Petersburg. Altogether, there were 13 speakers (9 male and 4 female, aged 1x 15-20, 7x 20-30, 4x 30-40, and 1x 40-50).
The recordings contain both monologues and dialogues on the topics of everyday life (health, education, family life, sports, culture) as well as information on both host countries (living abroad) and country of origin (Syria traditions, education system, etc.). Both types are spontaneous, the participants were given only the general subject and talked on the topic or discussed it freely. The transcription and translation team consisted of students of Arabic at Charles University, with an additional quality check provided by the native speakers of the dialect.
The textual data is split between the (parallel) transcriptions (.apc) and translations (.eng), with one segment per line. The additional .yaml file provides mapping to the corresponding audio file (with the duration and offset in the "%S.%03d" format, i.e., seconds and milliseconds) and a unique speaker ID.
The audio data is shared in the 48kHz .wav format, with dialogues and monologues in separate folders. All of the recordings are mono, with a single channel. For dialogues, there is a separate file for each speaker, e.g., "16072022_Family-01.wav" and "16072022_Family-02.wav".
The data provided in this repository corresponds to the test split of the dialectal Arabic to English shared task hosted at the 21st edition of the International Conference on Spoken Language Translation, i.e., IWSLT 2024.
Data from a questionnaire survey conducted from 2022-08-25 to 2022-11-15 and exploring the use of machine translation by Ukrainian refugees in the Czech Republic. The presented spreadsheet contains minimally processed data exported from the two questionnaires that were created in Google Forms in the Ukrainian and the Russian language. The links to these questionnaires were distributed by three methods: direct email to particular refugees whose contact details the authors obtained while volunteering; through a non-profit organisation helping refugees (Vesna women’s education institution) and on social networks by posting links to the survey in groups associating the Ukrainian community across Czech regions and towns.
Since we asked potential respondents to spread the questionnaire further, we could not prevent it from reaching Ukrainians who had arrived in Czechia previously, or received temporary protection in other countries. Due to this fact, the textual answers to the question 1.5 "Which country are you in right now?" were replaced in the dataset by numbers (1 for Czech Republic, 2 for other countries) in order for us to be able to separate the data of respondents not located in the Czech Republic, which were irrelevant for our survey.