QA4MRE (Reading Comprehension Q&A) by Kaggle | Other

About this Dataset

QA4MRE (Reading Comprehension Q&A)

Reading comprehension through Question & Answering

By Huggingface Hub [source]

About this dataset

The QA4MRE dataset offers a magnificent collection of passages with connected questions and answers, providing researchers with a defining set of data to work from. With its wide range, this has been the go-to source for many research projects like the CLEF 2011, 2012 and 2013 Shared Tasks - where training datasets are available for the main track as well as documents ready to be used in two pilot studies related to Alzheimer's disease and entrance exams. This expansive dataset can allow you to unleash your creativity in ways you never thought possible - uncovering new possibilities and exciting findings as it serves as an abundant source of information. No matter which field you come from or what kind of insights you’re looking for, this powerhouse dataset will have something special waiting just around the corner

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How to use the dataset

How to Use the QA4MRE Dataset for Your Research

The QA4MRE (Question Answering and Reading Comprehension) dataset is a great resource for researchers who want to use comprehensive datasets to explore creative approaches and solutions.
This powerful dataset provides several versions of training and development data in the form of passages with accompanying questions and answers. Additionally, there are gold standard documents included that can be used in two different pilot studies related to Alzheimer’s disease as well as entrance exams. The following is a guide on how to make the most out of this valuable data set:

Analyze Data Structures - Once you've downloaded all necessary materials, it’s time for analyzing what structure each file follows in order access its contents accordingly; knowing which column helps refine your searching process as some files go beyond just providing questions & answers such as providing topic names associated with passage text relevant processing question asking comprehension testing etc.. The table below serves as basic overview each column provided in both train & dev variants found within this datasets:

Column Name Description Datatype

Topic name Name of topic passage represents String

Refine Data Searching Process - Lastly if plan develop an automated system/algorithm uncover precise contents from manipulated articles/passages then refining already established search process involving

Column Name	Description	Datatype
Topic name	Name of topic passage represents	String

Research Ideas

Creating an automated question answering system that is capable of engaging in conversations with a user. This could be used as a teaching assistant to help students study for exams and other tests or as a virtual assistant for customer service.

Developing a summarization tool dedicated specifically to the QA4MRE dataset, which can extract key information from each passage and output concise summaries with confidence scores indicating the likelihood of the summary being accurate compared to the original text.

Utilizing natural language processing techniques to analyze questions related to Alzheimer’s disease and creating machine learning models that accurately predict patient responses when asked various sets of questions about their condition, thus aiding in diagnosing Alzheimer's Disease early on in its development stages

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 2012.main.AR_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

File: 2012.main.BG_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

File: 2013.main.BG_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

File: 2012.main.IT_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

File: 2012.alzheimers.EN_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

File: 2013.alzheimers.EN_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

File: 2011.main.EN_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

File: 2013.main.AR_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

File: 2013.main.EN_train.csv

Column name	Description
topic_name	The name of the topic associated with the passage. (String)
document_str	The text of the passage. (String)
question_str	The text of the question. (String)
answer_options	A list of possible answers. (List)
correct_answer_str	The correct answer. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.

Tables

N 2011 Main De Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2011_main_de_train

128.35 KB
120 rows
10 columns


CREATE TABLE n_2011_main_de_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2011 Main En Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2011_main_en_train

116.31 KB
120 rows
10 columns


CREATE TABLE n_2011_main_en_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2011 Main Es Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2011_main_es_train

125.51 KB
120 rows
10 columns


CREATE TABLE n_2011_main_es_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2011 Main It Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2011_main_it_train

126.81 KB
120 rows
10 columns


CREATE TABLE n_2011_main_it_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2011 Main Ro Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2011_main_ro_train

130.24 KB
120 rows
10 columns


CREATE TABLE n_2011_main_ro_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2012 Alzheimers En Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2012_alzheimers_en_train

96.98 KB
40 rows
10 columns


CREATE TABLE n_2012_alzheimers_en_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2012 Main Ar Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2012_main_ar_train

169.51 KB
160 rows
10 columns


CREATE TABLE n_2012_main_ar_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2012 Main Bg Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2012_main_bg_train

203.33 KB
160 rows
10 columns


CREATE TABLE n_2012_main_bg_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2012 Main De Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2012_main_de_train

159.07 KB
160 rows
10 columns


CREATE TABLE n_2012_main_de_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2012 Main En Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2012_main_en_train

138.54 KB
160 rows
10 columns


CREATE TABLE n_2012_main_en_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2012 Main Es Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2012_main_es_train

152.41 KB
160 rows
10 columns


CREATE TABLE n_2012_main_es_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2012 Main It Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2012_main_it_train

157.22 KB
160 rows
10 columns


CREATE TABLE n_2012_main_it_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2012 Main Ro Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2012_main_ro_train

157.36 KB
160 rows
10 columns


CREATE TABLE n_2012_main_ro_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2013 Alzheimers En Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2013_alzheimers_en_train

134.21 KB
40 rows
10 columns


CREATE TABLE n_2013_alzheimers_en_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2013 Entrance Exam En Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2013_entrance_exam_en_train

60.02 KB
46 rows
10 columns


CREATE TABLE n_2013_entrance_exam_en_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2013 Main Ar Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2013_main_ar_train

158.12 KB
284 rows
10 columns


CREATE TABLE n_2013_main_ar_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2013 Main Bg Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2013_main_bg_train

194.15 KB
284 rows
10 columns


CREATE TABLE n_2013_main_bg_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2013 Main En Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2013_main_en_train

137.07 KB
284 rows
10 columns


CREATE TABLE n_2013_main_en_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2013 Main Es Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2013_main_es_train

153.3 KB
284 rows
10 columns


CREATE TABLE n_2013_main_es_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);

N 2013 Main Ro Train

@kaggle.thedevastator_harness_the_challenges_of_qa4mre_in_your_researc.n_2013_main_ro_train

155.87 KB
284 rows
10 columns


CREATE TABLE n_2013_main_ro_train (
  "topic_id" BIGINT,
  "topic_name" VARCHAR,
  "test_id" BIGINT,
  "document_id" BIGINT,
  "document_str" VARCHAR,
  "question_id" BIGINT,
  "question_str" VARCHAR,
  "answer_options" VARCHAR,
  "correct_answer_id" BIGINT,
  "correct_answer_str" VARCHAR
);