Best Practices of Convolutional Neural Networks for Question Classification (original) (raw)
Related papers
A comparative analysis on question classification task based on deep learning approaches
PeerJ Computer Science
Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word em...
Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System
2018
Recently machine learning is being applied to almost every data domain one of which is Question Answering Systems (QAS). A typical Question Answering System is fairly an information retrieval system, which matches documents or text and retrieve the most accurate one. The idea of open domain question answering system put forth, involves convolutional neural network text classifiers. The Classification model presented in this paper is multi-class text classifier. The neural network classifier can be trained on large dataset. We report series of experiments conducted on Convolution Neural Network (CNN) by training it on two different datasets. Neural network model is trained on top of word embedding. Softmax layer is applied to calculate loss and mapping of semantically related words. Gathered results can help justify the fact that proposed hypothetical QAS is feasible. We further propose a method to integrate Convolutional Neural Network Classifier to an open domain question answering...
An Empirical Comparison of Question Classification Methods for Question Answering Systems
2020
Question classification is an important component of Question Answering Systems responsible for identifying the type of an answer a particular question requires. For instance, “Who is the prime minister of the United Kingdom?” demands a name of a PERSON, while “When was the queen of the United Kingdom born?” entails a DATE. This work makes an extensible review of the most recent methods for Question Classification, taking into consideration their applicability in low-resourced languages. First, we propose a manual classification of the current state-of-the-art methods in four distinct categories: low, medium, high, and very high level of dependency on external resources. Second, we applied this categorization in an empirical comparison in terms of the amount of data necessary for training and performance in different languages. In addition to complementing earlier works in this field, our study shows a boost on methods relying on recent language models, overcoming methods not suitab...
Question and Answer Classification in Czech Question Answering Benchmark Dataset
Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2019
In this paper, we introduce a new updated version of the Czech Question Answering database SQAD v2.1 (Simple Question Answering Database) with the update being devoted to improved question and answer classification. The SQAD v2.1 database contains more than 8,500 question-answer pairs with all appropriate metadata for QA training and evaluation. We present the details and changes in the database structure as well as a new algorithm for detecting the question type and the actual answer type from the text of the question. The algorithm is evaluated with more than 4,000 question answer pairs reaching the F1-measure of 88% for question typed and 85% for answer type detection.
International Journal of Electrical and Computer Engineering (IJECE), 2023
Question answering (QA) system nowadays is quite popular for automated answering purposes, the meaning analysis of the question plays an important role, directly affecting the accuracy of the system. In this article, we propose an improvement for question-answering models by adding more specific question analysis steps, including contextual characteristic analysis, pos-tag analysis, and question-type analysis built on deep learning network architecture. Weights of extracted words through question analysis steps are combined with the best matching 25 (BM25) algorithm to find the best relevant paragraph of text and incorporated into the QA model to find the best and least noisy answer. The dataset for the question analysis step consists of 19,339 labeled questions covering a variety of topics. Results of the question analysis model are combined to train the question-answering model on the data set related to the learning regulations of Industrial University of Ho Chi Minh City. It includes 17,405 pairs of questions and answers for the training set and 1,600 pairs for the test set, where the robustly optimized BERT pretraining approach (RoBERTa) model has an F1-score accuracy of 74%. The model has improved significantly. For long and complex questions, the mode has extracted weights and correctly provided answers based on the question's contents.
Integrating Question Classification and Deep Learning for improved Answer Selection
2018
We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.
Hybrid CNN-LSTM Model for Answer Identification
International journal of recent technology and engineering, 2019
User quest for information has led to development of Question Answer (QA) system to provide relevant answers to user questions. The QA task are different than normal NLP tasks as they heavily depend to semantics and context of given data. Retrieving and predicting answers to verity of questions require understanding of question, relevance with context and identifying and retrieving of suitable answers. Deep learning helps to produce impressive performance as it employs deep neural network with automatic feature extraction methods. The paper proposes a hybrid model to identify suitable answer for posed question. The proposes power exploits the power of CNN for extracting features and ability of LSTM for considering long term dependencies and semantic of context and question. Paper provides a comparative analysis on deep learning methods useful for predicting answer with the proposed method .The model is implemented on twenty tasks of babI dataset of Facebook .
CNN for Text-Based Multiple Choice Question Answering
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2018
The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for textbased multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets.
Deep Learning Approaches for Question Answering System
Procedia Computer Science, 2018
Question Answering (QA) System is very useful as most of the deep learning related problems can be modeled as a question answering problem. Consequently, the field is one of the most researched fields in computer science today. The last few years have seen considerable developments and improvement in the state of the art, much of which can be credited to upcoming of Deep Learning. In this paper, a discussion about various approaches starting from the basic NLP and algorithms based approach has been done and the paper eventually builds towards the recently proposed methods of Deep Learning. Implementation details and various tweaks in the algorithms that produced better results have also been discussed. The evaluation of the proposed models was done on twenty tasks of babI dataset of Facebook.
Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices
Journal of Xidian University, 2021
The usage and amount of information available on the internet increase over the past decade. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional knowledge sources. Such systems are designed to cater the most prominent answer from this giant knowledge source to the user's query using natural language understanding (NLU) and thus eminently depends on the Question-answering(QA) field. Question answering involves but not limited to the steps like mapping of user's question to pertinent query, retrieval of relevant information, finding the best suitable answer from the retrieved information etc. The current improvement of deep learning models evince compelling performance improvement in all these tasks. In this review work, the research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailing followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language. In the end, a survey of available datasets and evaluation measures is presented.