데이터셋 탐색

The IAM database contains 13,353 images of handwritten lines of text created by 657 writers. The texts those writers transcribed are from the Lancaster-Oslo/Bergen Corpus of British English. It includes contributions from 657 writers making a total of 1,539 handwritten pages comprising of 115,320 words and is categorized as part of modern collection. The database is labeled at the sentence, line, and word levels. Source: Measuring Human Perception to Improve Handwritten Document Transcription Image Source: https://fki.tic.heia-fr.ch/databases/iam-handwriting-database

A corpus of parallel text in 21 European languages from the proceedings of the European Parliament. The Europarl parallel corpus is extracted from the proceedings of the European Parliament (1996-2011). It includes versions in 21 European languages: Romanic (French, Italian, Spanish, Portuguese, Romanian), Germanic (English, Dutch, German, Danish, Swedish), Slavik (Bulgarian, Czech, Polish, Slovak, Slovene), Finni-Ugric (Finnish, Hungarian, Estonian), Baltic (Latvian, Lithuanian), and Greek. Parallel sentence counts are in the range 400K-2M, depending on the language combination. The goal of the extraction and processing was to generate sentence aligned text for statistical machine translation systems. For this purpose we extracted matching items and labeled them with corresponding document IDs. Using a preprocessor we identified sentence boundaries. We sentence aligned the data using a tool based on the Church and Gale algorithm. The Europarl corpus was collected mainly to aid research in statistical machine translation (training, evaluation), but it has been used for many other natural language problems: word sense disambiguation, anaphora resolution, information extraction, etc. Monolingual datasets are also available for 9 languages. These are supersets of the parallel versions. Monolingual word counts are in the range 7M-54M, depending on the language. Test Sets: Several test sets have been released for the Europarl corpus. In general, the Q4/2000 portion of the data (2000-10 to 2000-12) should be reserved for testing. All released test sets have been selected from this quarter. Source: Europarl website

The IAM database contains 13,353 images of handwritten lines of text created by 657 writers. The texts those writers transcribed are from the Lancaster-Oslo/Bergen Corpus of British English. It includes contributions from 657 writers making a total of 1,539 handwritten pages comprising of 115,320 words and is categorized as part of modern collection. The database is labeled at the sentence, line, and word levels. Source: Measuring Human Perception to Improve Handwritten Document Transcription Image Source: https://fki.tic.heia-fr.ch/databases/iam-handwriting-database

Abstract The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets. Keywords: plant disease prediction; classification; detection; dataset; survey; machine learning; deep learning

Diamante is a novel and efficient framework consisting of a data collection strategy and a learning method to boost the performance of pre-trained dialogue models. Two kinds of human feedback are collected and leveraged in Diamante, including explicit demonstration and implicit preference. The Diamante dataset is publicly available at the LUGE platform.

We introduce a large and comprehensive dataset to facilitate the study of several essential AM tasks in the debating system. In our work, we first review the existing subtasks (claim extraction, stance classification, evidence extraction), and then propose two integrated argument mining tasks: claim extraction with stance classification (CESC) and claim-evidence pair extraction (CEPE).

HiAML Computational Graph (CG) family introduced in "GENNAPE: Towards Generalized Neural Architecture Performance Estimators", accepted to AAAI-23. Contains 4.6k CIFAR-10 networks with an accuracy range of [91.11%, 93.44%].

The Norwegian Parliamentary Speech Corpus (NPSC) is a speech corpus made by the Norwegian Language Bank at the National Library of Norway in 2019-2021. The NPSC consists of recordings of speech from Stortinget, the Norwegian parliament, and corresponding orthographic transcriptions to Norwegian Bokmål and Norwegian Nynorsk. All transcriptions are done manually by trained linguists or philologists, and the manual transcriptions are subsequently proofread to ensure consistency and accuracy. Entire days of Parliamentary meetings are transcribed in the dataset.

DeepParliament is a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks. The dataset text covers a broad range of bills from 1986 to the present and contains richer information on parliament bill content. There are a total of 5329 documents where 4223 are in the train and 1106 are in the test dataset. Each bill document contains many sentences in both cases, and the document’s length varies greatly. Source: DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction Image Source: https://arxiv.org/pdf/2211.15424v1.pdf

Greek Parliament Proceedings is a curated dataset of the Greek Parliament Proceedings that extends chronologically from 1989 up to 2020. It consists of more than 1 million speeches with extensive metadata, extracted from 5,355 parliamentary record files. Source: A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis Image Source: https://arxiv.org/pdf/2210.12883v1.pdf

The Multi-Eup is a new multilingual benchmark dataset, comprising 22K multilingual documents collected from the European Parliament, spanning 24 languages. This dataset is designed to investigate fairness in a multilingual information retrieval (IR) context to analyze both language and demographic bias in a ranking context. It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages, as well as cross-lingual relevance judgments. Furthermore, it offers rich demographic information associated with its documents, facilitating the study of demographic bias.

A dataset of German parliament debates covering 74 years of plenary protocols across all 16 state parliaments of Germany as well as the German Bundestag. The debates are separated into individual speeches which are enriched with meta data identifying the speaker as a member of the parliament (mp). When using this data set, please cite the original paper "Lange, K.-R., Jentsch, C. (2023). SpeakGer: A meta-data enriched speech corpus of German state and federal parliaments. Proceedings of the 3rd Workshop on Computational Linguistics for Political Text Analysis@KONVENS 2023.". The meta data is separated into two different types: time-specific meta-data that contains only information for a legislative period but can change over time (e.g. the party or constituency of an mp) and meta-data that is considered fixed, such as the birth date or the name of a speaker. The former information are stored aong with the speeches as it is considered temporal information of that point in time, but are additionally stored in the file all_mps_mapping.csv if there is the need to double-check something. The rest of the meta-data are stored in the file all_mps_meta.csv. The meta-data from this file can be matched with a speech by comparing the speaker ID-variable "MPID". The speeches of each parliament are saved in a csv format. Along with the speeches, they contain the following meta-data: Period: int. The period in which the speech took place Session: int. The session in which the speech took place Chair: boolean. The information if the speaker was the chair of the plenary session Interjection: boolean. The information if the speech is a comment or an interjection from the crowd Party: list (e.g. ["cdu"] or ["cdu", "fdp"] when having more than one speaker during an interjection). List of the party of the speaker or the parties whom the comment/interjection references Consituency: string. The consituency of the speaker in the current legislative period MPID: int. The ID of the speaker, which can be used to get more meta-data from the file all_mps_meta.csv The file all_mps_meta.csv contains the following meta information: MPID: int. The ID of the speaker, which can be used to match the mp with his/her speeches. WikipediaLink: The Link to the mps Wikipedia page WikiDataLink: The Link to the mps WikiData page Name: string. The full name of the mp. Last Name: string. The last name of the mp, found on WikiData. If no last name is given on WikiData, the full name was heuristically cut at the last space to get the information neccessary for splitting the speeches. Born: string, format: YYYY-MM-DD. Birth date of the mp. If an exact birth date is found on WikiData, this exact date is used. Otherwise, a day in the year of birth given on Wikipedia is used. SexOrGender: string. Information on the sex or gender of the mp. Disclaimer: This infomation was taken from WikiData, which does not seem to differentiate between sex or gender. Occupation: list. Occupation(s) of the mp. Religion: string. Religious believes of the mp. AbgeordnetenwatchID: int. ID of the mp on the website Abgeordnetenwatch

Robbie Williams is a dataset of 65 songs by Robbie Williams. It consists of chords, keys and beats. The dataset does not include audio. Source: A BI-DIRECTIONAL TRANSFORMER FOR MUSICAL CHORD RECOGNITION Image Source: https://www.rwdb.info/