scholarly journals Efficient natural language classification algorithm for detecting duplicate unsupervised features

2021 ◽  
Vol 20 (3) ◽  
pp. 623-653
Author(s):  
Saud Altaf ◽  
Sofia Iqbal ◽  
Muhammad Waseem Soomro
Keyword(s):  
Natural Language ◽  
Short Term Memory ◽  
Short Term ◽  
Vocabulary Size ◽  
Language Understanding ◽  
Inverse Document Frequency ◽  
Classification Technique ◽  
Document Frequency ◽  
Text Features ◽  
Long Short Term Memory

This paper focuses on capturing the meaning of Natural Language Understanding (NLU) text features to detect the duplicate unsupervised features. The NLU features are compared with lexical approaches to prove the suitable classification technique. The transfer-learning approach is utilized to train the extraction of features on the Semantic Textual Similarity (STS) task. All features are evaluated with two types of datasets that belong to Bosch bug and Wikipedia article reports. This study aims to structure the recent research efforts by comparing NLU concepts for featuring semantics of text and applying it to IR. The main contribution of this paper is a comparative study of semantic similarity measurements. The experimental results demonstrate the Term Frequency–Inverse Document Frequency (TF-IDF) feature results on both datasets with reasonable vocabulary size. It indicates that the Bidirectional Long Short Term Memory (BiLSTM) can learn the structure of a sentence to improve the classification.

scholarly journals Natural language description of images using hybrid recurrent neural network

2019 ◽  
Vol 9 (4) ◽  
pp. 2932
Author(s):  
Md. Asifuzzaman Jishan ◽  
Khan Raqib Mahmud ◽  
Abul Kalam Al Azad
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Text Line ◽  
Short Term ◽  
Word Representation ◽  
Benchmark Datasets ◽  
Long Short Term Memory ◽  
Language Description

We presented a learning model that generated natural language description of images. The model utilized the connections between natural language and visual data by produced text line based contents from a given image. Our Hybrid Recurrent Neural Network model is based on the intricacies of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional Recurrent Neural Network (BRNN) models. We conducted experiments on three benchmark datasets, e.g., Flickr8K, Flickr30K, and MS COCO. Our hybrid model utilized LSTM model to encode text line or sentences independent of the object location and BRNN for word representation, this reduced the computational complexities without compromising the accuracy of the descriptor. The model produced better accuracy in retrieving natural language based description on the dataset.

Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Ralph Sherwin A. Corpuz ◽  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Natural Language ◽  
Text Categorization ◽  
Short Term Memory ◽  
Support Vector ◽  
Feature Engineering ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications.

Analyzing Natural Language Essay Generator Models Using Long Short-Term Memory Neural Networks

2021 ◽  
pp. 233-248
Author(s):  
Mayank Gaur ◽  
Mridul Arora ◽  
Varun Prakash ◽  
Yash Kumar ◽  
Kirti Gupta ◽  
...  
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals COVID-19 ChatBot

2021 ◽  
Vol 9 (11) ◽  
pp. 44-49
Author(s):  
Satish Tirumalapudi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
And Control ◽  
Prediction Problems

Abstract: Chat bots are software applications that help users to communicate with the machine and get the required result, this is where Natural Language Processing (NLP) comes into the picture. Natural language processing is based on deep learning that enables computers to acquire meaning from inputs given by the users. Natural language processing techniques can make possible the use of natural language to express ideas, thus drastically increasing accessibility. NLP engines rely on the elements of intent, utterance, entity, context, and session. Here in this project, we will be using Deep learning techniques which will be trained on the dataset which contains categories, patterns, and responses. Long Short-Term Memory (LSTM) is a Recurrent Neural Network that is capable of learning order dependence in sequence prediction problems. One of the most popular RNN approaches is LSTM to identify and control a dynamic system. We use an RNN to classify the category user’s message belongs to and then will give a response from the list of responses. Keywords: NLP – Natural Language Processing, LSTM – Long Short Term Memory, RNN – Recurrent Neural Networks.

scholarly journals Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia

2021 ◽  
Vol 10 (4) ◽  
pp. 354-361
Author(s):  
Yudi Widhiyasana ◽  
Transmissia Semiawan ◽  
Ilham Gibran Achmad Mudzakir ◽  
Muhammad Randi Noor
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Rate ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Bahasa Indonesia

Klasifikasi teks saat ini telah menjadi sebuah bidang yang banyak diteliti, khususnya terkait Natural Language Processing (NLP). Terdapat banyak metode yang dapat dimanfaatkan untuk melakukan klasifikasi teks, salah satunya adalah metode deep learning. RNN, CNN, dan LSTM merupakan beberapa metode deep learning yang umum digunakan untuk mengklasifikasikan teks. Makalah ini bertujuan menganalisis penerapan kombinasi dua buah metode deep learning, yaitu CNN dan LSTM (C-LSTM). Kombinasi kedua metode tersebut dimanfaatkan untuk melakukan klasifikasi teks berita bahasa Indonesia. Data yang digunakan adalah teks berita bahasa Indonesia yang dikumpulkan dari portal-portal berita berbahasa Indonesia. Data yang dikumpulkan dikelompokkan menjadi tiga kategori berita berdasarkan lingkupnya, yaitu “Nasional”, “Internasional”, dan “Regional”. Dalam makalah ini dilakukan eksperimen pada tiga buah variabel penelitian, yaitu jumlah dokumen, ukuran batch, dan nilai learning rate dari C-LSTM yang dibangun. Hasil eksperimen menunjukkan bahwa nilai F1-score yang diperoleh dari hasil klasifikasi menggunakan metode C-LSTM adalah sebesar 93,27%. Nilai F1-score yang dihasilkan oleh metode C-LSTM lebih besar dibandingkan dengan CNN, dengan nilai 89,85%, dan LSTM, dengan nilai 90,87%. Dengan demikian, dapat disimpulkan bahwa kombinasi dua metode deep learning, yaitu CNN dan LSTM (C-LSTM),memiliki kinerja yang lebih baik dibandingkan dengan CNN dan LSTM.

scholarly journals Extract Executable Action Sequences from Natural Language Instructions Based on DQN for Medical Service Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Fengda Zhao ◽  
Zhikai Yang ◽  
Xianshan Li ◽  
Dingding Guo ◽  
Haitao Li
Keyword(s):  
Natural Language ◽  
Short Term Memory ◽  
Average Length ◽  
Service Robots ◽  
Short Term ◽  
Medical Robots ◽  
Agent Based ◽  
Action Sequences ◽  
Long Short Term Memory ◽  
Better Than

The emergence and popularization of medical robots bring great convenience to doctors in treating patients. The core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. Language is the most convenient way for people to communicate with each other, so in this paper, a DQN agent based on long-short term memory (LSTM) and attention mechanism is proposed to enable the robots to extract executable action sequences from doctors’ natural language instructions. For this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. We evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. The results show that our agent can perform better than similar agents. And our agent has a better ability to handle long texts than previous works.

scholarly journals Explicit Knowledge-based Reasoning for Visual Question Answering

2017 ◽  
Author(s):  
Peng Wang ◽  
Qi Wu ◽  
Chunhua Shen ◽  
Anthony Dick ◽  
Anton van den Hengel
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Question Answering ◽  
Explicit Knowledge ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Visual Question Answering ◽  
Knowledge Based ◽  
Long Short Term Memory

We describe a method for visual question answering which is capable of reasoning about an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can explain the reasoning by which it developed its answer. It is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in testing. We also provide a dataset and a protocol by which to evaluate general visual question answering methods.

scholarly journals Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

2019 ◽  
Vol 11 (11) ◽  
pp. 237
Author(s):  
Jingren Zhang ◽  
Fang’ai Liu ◽  
Weizhi Xu ◽  
Hui Yu
Keyword(s):  
Natural Language ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Classification Model ◽  
Fusion Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing. However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual meaning of words and bidirectional long short-term memory (BiLSTM). The feature fusion model is divided into a multiple attention (MATT) CNN model and a bi-directional gated recurrent unit (BiGRU) model. The CNN model inputs the word vector (word vector attention, part of speech attention, position attention) that has been labeled by the attention mechanism into our multi-attention mechanism CNN model. Obtaining the influence intensity of the target keyword on the sentiment polarity of the sentence, and forming the first dimension of the sentiment classification, the BiGRU model replaces the original BiLSTM and extracts the global semantic features of the sentence level to form the second dimension of sentiment classification. Then, using PCA to reduce the dimension of the two-dimensional fusion vector, we finally obtain a classification result combining two dimensions of keywords and sentences. The experimental results show that the proposed MATT-CNN+BiGRU fusion model has 5.94% and 11.01% higher classification accuracy on the MRD and SemEval2016 datasets, respectively, than the mainstream CNN+BiLSTM method.

DLRCNeg: Deep Learning based Reading Comprehension by handling Negation

2019 ◽  
Author(s):  
Felicia Lilian J. ◽  
Sundarakantham K ◽  
Mercy Shalinie S.
Keyword(s):  
Reading Comprehension ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Activation Function ◽  
Attention Mechanism ◽  
Question Type ◽  
Short Term ◽  
Novel Approach ◽  
Long Short Term Memory

Question Answer (QA) System for Reading Comprehension (RC) is a computerized approach to retrieve relevant response to the query posted by the users. The underlined concept in developing such a system is to build a human computer interaction. The interactions will be in natural language and we tend to use negation words as a part of our expressions. During the pre-processing stage in Natural Language Processing (NLP) task these negation words gets removed and hence the semantics gets changed. This remains to be an unsolved problem in QA system. In order to maintain the semantics we have proposed a novel approach Hybrid NLP based Bi-directional Long Short Term Memory (Bi-LSTM) with attention mechanism. It deals with the negation words and maintains the semantics of the sentence. We also focus on answering any factoid query (i.e. ’what’, ’when’, ’where’, ’who’) that is raised by the user. For this purpose, the use of attention mechanism with softmax activation function has obtained superior results that matches the question type and process the context information effectively. The experimental results are performed over the SQuAD dataset for reading comprehension and the Stanford Negation dataset is used to perform the negation in the RC sentence. The accuracy of the system over negation is obtained as 93.9% and over the QA system is 87%.

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