Text Classification For QA Dataset

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Text Classification for QA Dataset

Text classification dataset for question answering

By uva-irlab (From Huggingface) [source]

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About this dataset

The Text Classification for Question Answering dataset is a collection of data that is specifically designed for training and evaluating text classification models meant for answering questions. The dataset contains multiple columns that provide various types of information to facilitate this task.

One important aspect of the dataset is the presence of previous questions, which can help provide context and background information for the current question being asked. These previous questions allow the model to understand the conversation flow and potentially improve its performance in generating accurate answers.

The current question being asked is another crucial component of the dataset. This column represents the specific question that needs to be answered based on the available information.

To assist in determining relevant terms or keywords, there are gold terms provided in another column. These gold terms are considered correct or relevant for answering the question effectively. They serve as reference points or guidelines for evaluating model performance.

Semantic terms are also included in a separate column, which provides additional context by identifying related concepts or ideas connected to the question being asked. These semantic terms can further aid in understanding and generating accurate answers.

Another element provided by this dataset is overlapping terms between the question and answer text, offering insights into common keywords shared by both elements. This overlap could signify important concepts that are likely to be addressed in crafting an appropriate response.

The answer text with window column gives not only the answer but also includes some surrounding context from which it was derived. This allows models to consider broader context when formulating responses rather than relying strictly on isolated answers.

Furthermore, named entities recognized by BERT (Bidirectional Encoder Representations from Transformers) model are highlighted through BERT NER overlap column if they appear both in questions and answers. Identifying these named entities can enhance comprehension and generation of more accurate responses within specific entity contexts.

By using this comprehensive Text Classification for Question Answering dataset, researchers can train their models effectively, evaluate their performance on validation data, fine-tune them accordingly using training data, and ultimately test their models' effectiveness on the provided test data

How to use the dataset

• Introduction:

• Understanding the Dataset:
  The dataset consists of several columns that contain relevant information:

  • prev_questions: This column contains the previous questions asked in the conversation.
  • cur_question: This column contains the current question being asked.
  • gold_terms: These are the terms considered correct or relevant for answering each question.
  • semantic_terms: These are terms semantically related to each question.
  • overlapping_terms: These are terms that overlap between each question and its corresponding answer.
  • answer_text_with_window: This column provides the answer text along with some surrounding context.
  • bert_ner_overlap: Named entities recognized by BERT model that overlap between each question and its corresponding answer.

• Using the Dataset:

• Training Phase:
  To train a text classification model, you can utilize train.csv file which contains data exclusively for this purpose. You can explore different techniques such as deep learning models, traditional machine learning algorithms like Random Forests, or even pre-trained language models (e.g., BERT) to build your classification model.

• Validation Phase:
  For evaluating your trained models' performance, use validation.csv file which consists of test data specifically designed to validate how well your model generalizes on unseen samples from within original input distribution covered in training phase.

• Testing Phase:
  Once you have validated your trained model's performance using validation data, it's time to evaluate its effectiveness on new and unseen samples from outside original input distribution where no prior knowledge was available during training phase. Use test.csv file to test your final text classification model's performance on this unseen data.

• Best Practices:
  Here are some best practices when using this dataset:

  • Data Preprocessing: As with any text classification task, it is important to preprocess the text data before training your model. This may involve steps like tokenization, lowercasing, removing stop words, and handling punctuation marks or special characters.
  • Feature Engineering: Consider extracting meaningful features from the raw text data that can enhance the performance of your model. This may include n-gram features, part-of-speech tags, or syntactic dependencies.
  • Model Selection: Experiment with different models and architectures to find the

Research Ideas

• Text classification model training: This dataset can be used to train a text classification model for question answering. The columns in the dataset provide relevant information such as previous questions, current question, gold terms, semantic terms, overlapping terms, and answer text with window. By using this data, one can build a model that can accurately classify questions and provide relevant answers.
• Performance validation: The dataset also includes a validation set (validation.csv) which can be used to evaluate the performance of a text classification model for question answering. By using this set of labeled data, one can assess the accuracy and effectiveness of their model before deploying it.
• Model testing: The test set (test.csv) in the dataset can be used to test the performance of a trained text classification model for question answering on unseen data. This allows one to evaluate how well their trained model generalizes and performs on new instances.
  Overall, this dataset provides an opportunity to explore and develop various approaches and techniques for text classification in the context of question answering tasks

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: validation.csv

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File: train.csv

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File: test.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit uva-irlab (From Huggingface).