id stringlengths 20 52 | title stringlengths 3 459 | abstract stringlengths 0 12.3k | classification_labels list | numerical_classification_labels list |
|---|---|---|---|---|
SCOPUS_ID:85112217361 | A Deep Learning Approach Toward Determining the Effects of News Trust Factor Based on Source Polarity | Fake news is one of the biggest threats in cyber-world nowadays. There are several categories of fake news like clickbait, propaganda, satire/parody, sloppy journalism, misleading headings, biased or slanted news. Now, due to limited time available generally to the readers, they are subjected to few of these form of fa... | [
"Polarity Analysis",
"Ethical NLP",
"Sentiment Analysis",
"Reasoning",
"Fact & Claim Verification",
"Responsible & Trustworthy NLP"
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33,
17,
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8,
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4
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SCOPUS_ID:85121808442 | A Deep Learning Approach for Aspect Sentiment Triplet Extraction in Portuguese | Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence “Large rooms and great breakfast”, ASTE outputs the triplet T = {(rooms, lar... | [
"Information Extraction & Text Mining",
"Aspect-based Sentiment Analysis",
"Sentiment Analysis"
] | [
3,
23,
78
] |
http://arxiv.org/abs/2005.04938v1 | A Deep Learning Approach for Automatic Detection of Fake News | Fake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning f... | [
"Reasoning",
"Fact & Claim Verification",
"Ethical NLP",
"Responsible & Trustworthy NLP"
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8,
46,
17,
4
] |
SCOPUS_ID:85126259808 | A Deep Learning Approach for Bangla Image Captioning System | Naturalness and generalization are the two challenges while generating automated Image Captioning through a System. There is a lack of research to focus on these challenges for Image Captioning in the Bangla language. Furthermore, the lexical resources for Image Captioning in Bangla are not adequate. An effort has been... | [
"Visual Data in NLP",
"Language Models",
"Semantic Text Processing",
"Captioning",
"Text Generation",
"Multimodality"
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20,
52,
72,
39,
47,
74
] |
SCOPUS_ID:85116743553 | A Deep Learning Approach for Classifying Vulnerability Descriptions Using Self Attention Based Neural Network | Cyber threat intelligence (CTI) refers to essential knowledge used by organizations to prevent or mitigate against cyber attacks. Vulnerability databases such as CVE and NVD are crucial to cyber threat intelligence, but also provide information leveraged in hundreds of security products worldwide. However, previous stu... | [
"Information Extraction & Text Mining",
"Text Classification",
"Robustness in NLP",
"Information Retrieval",
"Responsible & Trustworthy NLP"
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3,
36,
58,
24,
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SCOPUS_ID:85116942156 | A Deep Learning Approach for Dengue Tweet Classification | Dengue is one amongst the foremost widespread vector borne diseases best-known these days. According to National Institute of Allergy and Infectious Disease (NIAID), Dengue fever has been identified as a threat to public health [1]. More than 33% of the total world population is under risk, together with several cities... | [
"Information Retrieval",
"Text Classification",
"Information Extraction & Text Mining"
] | [
24,
36,
3
] |
http://arxiv.org/abs/1711.05350v1 | A Deep Learning Approach for Expert Identification in Question Answering Communities | In this paper, we describe an effective convolutional neural network framework for identifying the expert in question answering community. This approach uses the convolutional neural network and combines user feature representations with question feature representations to compute scores that the user who gets the high... | [
"Natural Language Interfaces",
"Question Answering"
] | [
11,
27
] |
http://arxiv.org/abs/1803.00344v1 | A Deep Learning Approach for Multimodal Deception Detection | Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multi-modal neural model for deception detection. By combining features from different modalities such as video, audio, and tex... | [
"Visual Data in NLP",
"Multimodality"
] | [
20,
74
] |
http://arxiv.org/abs/2112.08554v1 | A Deep Learning Approach for Ontology Enrichment from Unstructured Text | Information Security in the cyber world is a major cause for concern, with a significant increase in the number of attack surfaces. Existing information on vulnerabilities, attacks, controls, and advisories available on the web provides an opportunity to represent knowledge and perform security analytics to mitigate so... | [
"Knowledge Representation",
"Semantic Text Processing",
"Robustness in NLP",
"Responsible & Trustworthy NLP"
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18,
72,
58,
4
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SCOPUS_ID:85080109534 | A Deep Learning Approach for Optical Character Recognition of Handwritten Devanagari Script | Handwritten Character Recognition is one of the most challenging and demanding area of interest for researchers in domains of pattern recognition and image processing. Many researchers have worked with recognition of characters of different languages but there is comparatively less work carried for Devanagari Script. I... | [
"Visual Data in NLP",
"Multimodality"
] | [
20,
74
] |
SCOPUS_ID:85060014650 | A Deep Learning Approach for Part-of-Speech Tagging in Nepali Language | Part of Speech (POS) tagging is the most fundamental task in various natural language processing(NLP) applications such as speech recognition, information extraction and retrieval and so on. POS tagging involves annotation of appropriate tag for each token in the corpus based on its context and the syntax of the langua... | [
"Tagging",
"Syntactic Text Processing"
] | [
63,
15
] |
SCOPUS_ID:85133518598 | A Deep Learning Approach for Plagiarism Detection System Using BERT | The processing of natural language processing is changed after the evident of deep learning algorithms. The machine learning algorithms use numerical data for processing; therefore, categorical data are converted into equivalent vectors for processing by the machines. Word embeddings are the real vectored representatio... | [
"Language Models",
"Semantic Text Processing",
"Representation Learning"
] | [
52,
72,
12
] |
SCOPUS_ID:85137266042 | A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic | Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsA... | [
"Sentiment Analysis"
] | [
78
] |
SCOPUS_ID:85150217712 | A Deep Learning Approach for Recognizing Textual Emotion from Bengali-English Code-Mixed Data | Emotion detection is a computational approach for finding the distinct emotion or feeling of an individual. Although Bengali is a low-resource language, the amount of Bengali-English codemixed textual data has grown significantly because of the recent widespread use of social media applications among Bengali users. Gra... | [
"Language Models",
"Programming Languages in NLP",
"Semantic Text Processing",
"Representation Learning",
"Multimodality"
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52,
55,
72,
12,
74
] |
SCOPUS_ID:85103286573 | A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers | During the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users. In this scenario, the disruption of fake news providers and bot accounts for spreading propaganda information as well ... | [
"Language Models",
"Semantic Text Processing",
"Robustness in NLP",
"Ethical NLP",
"Responsible & Trustworthy NLP",
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SCOPUS_ID:85141868073 | A Deep Learning Approach for Robust, Multi-oriented, and Curved Text Detection | Automatic text localization and segmentation in a normal environment with vertical or curved texts are core elements of numerous tasks comprising the identification of vehicles and self-driving cars, and preparing significant information from real scenes to visually impaired people. Nevertheless, texts in the real envi... | [
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"Robustness in NLP",
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21,
74
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SCOPUS_ID:85128477505 | A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews | User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period ... | [
"Language Models",
"Semantic Text Processing",
"Sentiment Analysis"
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52,
72,
78
] |
SCOPUS_ID:85102411519 | A Deep Learning Approach for Text Segmentation in Document Analysis | Text segmentation plays an essential role in both page segmentation and document reading comprehension. In this manuscript, we present a system to separate the page into homogeneous regions that can serve to extract information. Our approach is based on the U-Net network platform to extract text-lines, then the text li... | [
"Visual Data in NLP",
"Text Segmentation",
"Syntactic Text Processing",
"Multimodality"
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20,
21,
15,
74
] |
SCOPUS_ID:85092637841 | A Deep Learning Approach of Collaborative Filtering to Recommender System with Opinion Mining | To produce good quality recommendations for large or enterprise scale problems, a competent approach for recommender system is required. This paper presents such an approach which first generates the text score based on users’ reviews with the help of opinion mining. It then feeds ratings corresponding to the text scor... | [
"Opinion Mining",
"Sentiment Analysis"
] | [
49,
78
] |
http://arxiv.org/abs/1409.8558v1 | A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis | Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficients as the vocal tract parameterization of the speech signal. Mel Cepstral coefficients were never intended to work in a parametric speech synthesis framework, but as yet, there has been little success in creating a better parameteriz... | [
"Responsible & Trustworthy NLP",
"Multimodality",
"Speech & Audio in NLP",
"Green & Sustainable NLP"
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4,
74,
70,
68
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SCOPUS_ID:85105957301 | A Deep Learning Approach to Distinguish 2019-nCoV and SARS-CoV Sequences | This paper presents a classification of protein sequences obtained from the 2019 Novel Coronavirus (2019n-CoV) and the 2003 SARS Coronavirus (SARS-CoV) using natural language processing. Very recent researches have indicated that the 2019-nCoV bears almost 79% sequence identity to the SARS-CoV but is sufficiently uniqu... | [
"Information Retrieval",
"Text Classification",
"Information Extraction & Text Mining"
] | [
24,
36,
3
] |
SCOPUS_ID:85097298252 | A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching | Recognizing toponyms and resolving them to their real-world referents is required to provide advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a previously... | [
"Cross-Lingual Transfer",
"Multilinguality"
] | [
19,
0
] |
http://arxiv.org/abs/2201.02735v1 | A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot | In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to unders... | [
"Natural Language Interfaces",
"Dialogue Systems & Conversational Agents"
] | [
11,
38
] |
https://aclanthology.org//W09-0438/ | A Deep Learning Approach to Machine Transliteration | [
"Machine Translation",
"Text Generation",
"Multilinguality"
] | [
51,
47,
0
] | |
SCOPUS_ID:85062547231 | A Deep Learning Approach to Sentiment Analysis in Turkish | This study proposes using deep learning for sentiment analysis in Turkish. Traditional machine learning methods such as logistic regression or Naive Bayes are often applied to this problem however their applicability is limited since they use bag-of-words model which does not take into account the order of the words in... | [
"Sentiment Analysis"
] | [
78
] |
SCOPUS_ID:85138252336 | A Deep Learning Approach to UML Class Diagrams Discovery from Textual Specifications of Software Systems | Software engineering has developed tools to streamline developing software systems. Among these, the model-driven architecture proposes going from specifications written in natural language to the application code via two intermediate models: a platform-independent model and a platform-specific model. Since the models ... | [
"Coreference Resolution",
"Information Retrieval",
"Text Classification",
"Information Extraction & Text Mining"
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13,
24,
36,
3
] |
SCOPUS_ID:85136925142 | A Deep Learning Approach to Solving Morphological Analogies | Analogical proportions are statements of the form “A is to B as C is to D”. They support analogical inference and provide a logical framework to address learning, transfer, and explainability concerns. This logical framework finds useful applications in AI and natural language processing (NLP). In this paper, we addres... | [
"Semantic Text Processing",
"Morphology",
"Syntactic Text Processing",
"Representation Learning",
"Reasoning"
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72,
73,
15,
12,
8
] |
SCOPUS_ID:85078340708 | A Deep Learning Approach with Deep Contextualized Word Representations for Chemical-Protein Interaction Extraction from Biomedical Literature | Mining interactions between chemicals and proteins/genes is of crucial relevance for clinical medicine, adverse drug effects, and pharmacological research. Although chemical-protein interactions (CPIs) can be manually extracted, this process is expensive and time-consuming. Therefore, it is of considerable significance... | [
"Representation Learning",
"Language Models",
"Semantic Text Processing",
"Information Extraction & Text Mining"
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12,
52,
72,
3
] |
SCOPUS_ID:85092112146 | A Deep Learning Architecture with Word Embeddings to Classify Sentiment in Twitter | Social Media Networks are one of the main platforms to express our feelings. The emotions we put in text tell a lot about our behavior towards any topic. Therefore, the analysis of text is a need for detecting one’s emotions in many fields. This paper introduces a deep learning model that classify sentiments from tweet... | [
"Language Models",
"Semantic Text Processing",
"Information Retrieval",
"Representation Learning",
"Sentiment Analysis",
"Text Classification",
"Information Extraction & Text Mining"
] | [
52,
72,
24,
12,
78,
36,
3
] |
SCOPUS_ID:85115062402 | A Deep Learning Based Approach for Classification of News as Real or Fake | In recent past, the growth of both the printed and digital media has greatly facilitated the business and the society. On account of the reach of social media, even the smallest news or event could be spread like wildfire. Often due to this, the news gets amplified and distorted drastically resulting in generation of f... | [
"Language Models",
"Semantic Text Processing",
"Information Retrieval",
"Information Extraction & Text Mining",
"Ethical NLP",
"Reasoning",
"Fact & Claim Verification",
"Text Classification",
"Responsible & Trustworthy NLP"
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52,
72,
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3,
17,
8,
46,
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4
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SCOPUS_ID:85135143854 | A Deep Learning Based Approach to Structural Function Recognition of Scientific Literature Abstracts | [Purpose/Significance] Abstracts of scientific documents are often composed of sections with specific functions. Using the deep learning method to identify structural functions of abstracts of scientific documents is conducive to the in-depth analysis of the documents. [Method/Process] In this paper, identifying struct... | [
"Language Models",
"Semantic Text Processing",
"Text Classification",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
52,
72,
36,
24,
3
] |
http://arxiv.org/abs/1910.06707v1 | A Deep Learning Based Chatbot for Campus Psychological Therapy | In this paper, we propose Evebot, an innovative, sequence to sequence (Seq2seq) based, fully generative conversational system for the diagnosis of negative emotions and prevention of depression through positively suggestive responses. The system consists of an assembly of deep-learning based models, including Bi-LSTM b... | [
"Language Models",
"Natural Language Interfaces",
"Semantic Text Processing",
"Dialogue Systems & Conversational Agents"
] | [
52,
11,
72,
38
] |
SCOPUS_ID:85108009883 | A Deep Learning Based Method for Structuring the Chinese Pathological Reports of Lung Specimen | As a kind of electronic reports in text form, the Chinese pathology report of lung specimen contains a large amount of information that is important for clinicians to further analysis and mining. However, various expressions and no fixed format increases the difficulty of extracting and standardizing this information. ... | [
"Tagging",
"Syntactic Text Processing"
] | [
63,
15
] |
SCOPUS_ID:85127120709 | A Deep Learning Based Methodology for Information Extraction from Documents in Robotic Process Automation | In recent years, thanks to Optical Character Recognition techniques and technologies to deal with low scan quality and complex document structure, there has been a continuous evolution and automation of the digitization processes to allow Robotic Process Automation. In this paper we propose a methodology based both on ... | [
"Visual Data in NLP",
"Multimodality",
"Information Extraction & Text Mining"
] | [
20,
74,
3
] |
SCOPUS_ID:85077006773 | A Deep Learning Based Reasoner for Global Consistency in Named Entity Recognition | Named Entity Recognition (NER) is a basic task of Natural Language Processing (NLP), it’s a challenging task in a variety of special applications. This paper aims to solve the global consistency of NER, and to improve the performance. Inspired by human reading process, we propose a NE-Reasoner model, which combine deep... | [
"Named Entity Recognition",
"Reasoning",
"Information Extraction & Text Mining"
] | [
34,
8,
3
] |
SCOPUS_ID:85098255765 | A Deep Learning Classification Approach for Short Messages Sentiment Analysis | In today's world, we humans have been communicating with each other through calls, social media applications like whatsapp, facebook, twitter etc. From the social media apps we get social media data from those applications and check what sentences are positive and negative sentiment using sentiment analysis and using d... | [
"Information Extraction & Text Mining",
"Information Retrieval",
"Text Classification",
"Sentiment Analysis"
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3,
24,
36,
78
] |
SCOPUS_ID:85141170667 | A Deep Learning Ensemble Hate Speech Detection Approach for Sinhala Tweets | We live in an era where social media platforms play a key role in society. These platforms support most of the native languages and this has enabled people to express their opinions conveniently. Also, it is very common to observe that people express very hateful opinions on social media platforms as well. Several stud... | [
"Ethical NLP",
"Responsible & Trustworthy NLP"
] | [
17,
4
] |
SCOPUS_ID:85100578498 | A Deep Learning Framework for Automatic Detection of Hate Speech Embedded in Arabic Tweets | In this paper, we investigate the ability of CNN, CNN-LSTM, and BiLSTM-CNN deep learning networks to automatically classify or discover hateful content posted on social media. These deep networks were trained and tested using ArHS dataset which consists of 9833 tweets that were annotated to suite hateful speech detecti... | [
"Language Models",
"Semantic Text Processing",
"Information Retrieval",
"Information Extraction & Text Mining",
"Ethical NLP",
"Text Classification",
"Responsible & Trustworthy NLP"
] | [
52,
72,
24,
3,
17,
36,
4
] |
SCOPUS_ID:85018289905 | A Deep Learning Framework for Coreference Resolution Based on Convolutional Neural Network | Recently many researches have shown that word embeddings are able to represent information from word related contexts or its nearest neighborhood words, and thus are applied in many NLP tasks successfully. In this paper, we propose convolutional neural network model to extent word embeddings to mention/antecedent repre... | [
"Coreference Resolution",
"Semantic Text Processing",
"Information Extraction & Text Mining",
"Representation Learning"
] | [
13,
72,
3,
12
] |
SCOPUS_ID:85130354841 | A Deep Learning Framework for Detection of COVID-19 Fake News on Social Media Platforms | The fast growth of technology in online communication and social media platforms alleviated numerous difficulties during the COVID-19 epidemic. However, it was utilized to propagate falsehoods and misleading information about the disease and the vaccination. In this study, we investigate the ability of deep neural netw... | [
"Language Models",
"Semantic Text Processing",
"Ethical NLP",
"Reasoning",
"Fact & Claim Verification",
"Responsible & Trustworthy NLP"
] | [
52,
72,
17,
8,
46,
4
] |
SCOPUS_ID:85141663947 | A Deep Learning Method for Sentence Embeddings Based on Hadamard Matrix Encodings | Sentence Embedding is recently getting an accrued attention from the Natural Language Processing (NLP) community. An embedding maps a sentence to a vector of real numbers with applications to similarity and inference tasks. Our method uses: word embeddings, dependency parsing, Hadamard matrix with spread spectrum algor... | [
"Semantic Text Processing",
"Syntactic Text Processing",
"Representation Learning",
"Syntactic Parsing",
"Reasoning",
"Textual Inference"
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72,
15,
12,
28,
8,
22
] |
SCOPUS_ID:85121358905 | A Deep Learning Model Based on BERT and Sentence Transformer for Semantic Keyphrase Extraction on Big Social Data | In the evolution of the Internet, social media platform like Twitter has permitted the public user to share information such as famous current affairs, events, opinions, news, and experiences. Extracting and analyzing keyphrases in Twitter content is an essential and challenging task. Keyphrases can become precise the ... | [
"Language Models",
"Semantic Text Processing",
"Term Extraction",
"Information Extraction & Text Mining"
] | [
52,
72,
1,
3
] |
SCOPUS_ID:85113767336 | A Deep Learning Model Based on Neural Bag-of-Words Attention for Sentiment Analysis | In the field of Natural Language Processing, sentiment analysis is one of core research directions. The hot issue of sentiment analysis is how to avoid the shortcoming of using fixed vector to calculate attention distribution. In this paper, we proposed a novel sentiment analysis model based on neural bag-of-words atte... | [
"Sentiment Analysis"
] | [
78
] |
SCOPUS_ID:85021762829 | A Deep Learning Model Enhanced with Emotion Semantics for Microblog Sentiment Analysis | Word embedding based on neural language model can automatically learn effective word representation from massive unlabeled text dataset, and has made essential progress in many natural language processing tasks. Emoticons in microblog are important emotion signals for microblog sentiment analysis. There have been a lot... | [
"Semantic Text Processing",
"Information Retrieval",
"Representation Learning",
"Sentiment Analysis",
"Emotion Analysis",
"Text Classification",
"Information Extraction & Text Mining"
] | [
72,
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78,
61,
36,
3
] |
SCOPUS_ID:85125179063 | A Deep Learning Model Fused with Word Sense Knowledge for Textual Entailment Recognition | Textual entailment recognition is an essential research task in the field of natural language processing. The mainstream textual entailment recognition method based on deep learning does not integrate word sense knowledge in training data, so the inference knowledge of model learning is limited. In addition, polysemy h... | [
"Reasoning",
"Semantic Text Processing",
"Textual Inference",
"Representation Learning"
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8,
72,
22,
12
] |
SCOPUS_ID:85077780895 | A Deep Learning Model for Dimensional ValenceArousal Intensity Prediction in Stock Market | This paper proposes a dimensional valence-arousal method to define sentiment status in the stock market. In the past, many kinds of research have focused on the valence sentiment on stock messages because it represents the stock trend such as upward and downward. In this case, if the stock price jumps or collapses (pos... | [
"Sentiment Analysis"
] | [
78
] |
SCOPUS_ID:85143373485 | A Deep Learning Model for Opinion mining in Twitter Combining Text and Emojis | Several approaches have been proposed to study opinions on Social Network Sites (SNS). Unfortunately, those works are not topic-sensitive and do not investigate the impact of emojis on text-based classification. In this paper, we propose a novel approach to predict the users' opinions expressed through textual tweets a... | [
"Visual Data in NLP",
"Opinion Mining",
"Sentiment Analysis",
"Multimodality"
] | [
20,
49,
78,
74
] |
SCOPUS_ID:85132914040 | A Deep Learning Modified Neural Network(DLMNN) based proficient sentiment analysis technique on Twitter data | The rapid enhancement in social media over the internet generates massive information in real-time scenarios, which has a striking impact on big data analysis. It resulted in the elevated usage of emotions and sentiments in social media. This paper proffers a proficient sentiment analysis technique in Twitter data. The... | [
"Sentiment Analysis"
] | [
78
] |
SCOPUS_ID:85085529136 | A Deep Learning Sentiment Primarily Based Intelligent Product Recommendation System | In recent years, technological enhancements in computing have semiconductor to the event of delicate call support systems to produce support to the purchasers United Nations agency ar victimization social networks for obtaining services. At intervals the past, sure researchers classified product and building reviews in... | [
"Text Classification",
"Text Clustering",
"Sentiment Analysis",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
36,
29,
78,
24,
3
] |
SCOPUS_ID:85123638801 | A Deep Learning System for Automatic Extraction of Typological Linguistic Information from Descriptive Grammars | Linguistic typology is an area of linguistics concerned with analysis of and comparison between natural languages of the world based on their certain linguistic features. For that purpose, historically, the area has relied on manual extraction of linguistic feature values from textural descriptions of languages. This m... | [
"Multilinguality",
"Typology",
"Syntactic Text Processing",
"Information Extraction & Text Mining"
] | [
0,
45,
15,
3
] |
http://arxiv.org/abs/2303.10510v1 | A Deep Learning System for Domain-specific speech Recognition | As human-machine voice interfaces provide easy access to increasingly intelligent machines, many state-of-the-art automatic speech recognition (ASR) systems are proposed. However, commercial ASR systems usually have poor performance on domain-specific speech especially under low-resource settings. The author works with... | [
"Language Models",
"Semantic Text Processing",
"Speech & Audio in NLP",
"Text Generation",
"Speech Recognition",
"Multimodality"
] | [
52,
72,
70,
47,
10,
74
] |
http://arxiv.org/abs/2004.10320v1 | A Deep Learning System for Sentiment Analysis of Service Calls | Sentiment analysis is crucial for the advancement of artificial intelligence (AI). Sentiment understanding can help AI to replicate human language and discourse. Studying the formation and response of sentiment state from well-trained Customer Service Representatives (CSRs) can help make the interaction between humans ... | [
"Sentiment Analysis"
] | [
78
] |
http://arxiv.org/abs/1911.01421v1 | A Deep Learning approach for Hindi Named Entity Recognition | Named Entity Recognition is one of the most important text processing requirement in many NLP tasks. In this paper we use a deep architecture to accomplish the task of recognizing named entities in a given Hindi text sentence. Bidirectional Long Short Term Memory (BiLSTM) based techniques have been used for NER task in... | [
"Language Models",
"Semantic Text Processing",
"Representation Learning",
"Named Entity Recognition",
"Information Extraction & Text Mining"
] | [
52,
72,
12,
34,
3
] |
SCOPUS_ID:85113381472 | A Deep Learning based Customer Sentiment Analysis Model to Enhance Customer Retention and Loyalty in the Payment Industry | Both the industry and academia agree on the immense contribution of big data analytics and machine learning to competitive businesses. The payment industry would benefit from big data analytics and machine learning capabilities to harness their customers' opinions through sentiment analysis, thereby customizing their s... | [
"Sentiment Analysis"
] | [
78
] |
SCOPUS_ID:85099568005 | A Deep Learning based Interlingua Representation for Malayalam Documents | Compact representation of sentences like feature vectors, offer better understanding of the sentence formation. Majority applications in natural language processing often requires the help of such meaningful representations. If an interlingua is constructed for the same, it will be useful for the applications like mach... | [
"Multilinguality",
"Machine Translation",
"Semantic Text Processing",
"Representation Learning",
"Summarization",
"Text Generation",
"Information Extraction & Text Mining"
] | [
0,
51,
72,
12,
30,
47,
3
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SCOPUS_ID:85124692911 | A Deep Learning based Self-Assessment Tool for Personality Traits and Interview Preparations | Many people face difficulty in analysing their own personality and to see whether they fit a particular job profile. Analysing our personality is very crucial, especially as a part of preparing for various types of interviews, as our responses reflects how we think and act, thus imprinting our first impression on the p... | [
"Visual Data in NLP",
"Multimodality",
"Sentiment Analysis"
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20,
74,
78
] |
SCOPUS_ID:85106638500 | A Deep Learning based Sentiment Analysis on Bang-lish Disclosure | Sentiment analysis is a field of immense possibilities and application despite being an age-old topic. Various applications of machine learning and natural language processing keep contributing to this field with innovative techniques. Variants of neural networks with attention mechanism is a well-known tool in this fi... | [
"Sentiment Analysis"
] | [
78
] |
SCOPUS_ID:85141663516 | A Deep Learning based hybrid model for improving accuracy of sentiment analysis | Text Sentiment analysis has been of great importance over the last few years. It is being widely used to determine a person's feelings, opinions, and emotions on any topic or for someone. In recent years CNN and LSTM have been widely used to develop such models. CNN has shown that it can effectively extract local infor... | [
"Language Models",
"Semantic Text Processing",
"Sentiment Analysis"
] | [
52,
72,
78
] |
SCOPUS_ID:85098274433 | A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds | Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several in vitro methods have been develo... | [
"Information Extraction & Text Mining"
] | [
3
] |
SCOPUS_ID:85145652536 | A Deep Learning-Based Entity-Relationship Extraction Method in the Field of Electric Power Public Opinion | Entity-relationship extraction can obtain key information elements from texts. Electricity opinion texts have the characteristics of complex entity relationships and less annotated data, so it is difficult to find entity information with mutual relationships from text data in the field of electricity opinion. To solve ... | [
"Language Models",
"Relation Extraction",
"Semantic Text Processing",
"Information Extraction & Text Mining"
] | [
52,
75,
72,
3
] |
SCOPUS_ID:85147846574 | A Deep Learning-Based Innovative Points Extraction Method | Most of the research on mining online reviews now focuses on the influence of reviews on consumers and the issue of sentiment analysis for analyzing consumer reviews, but few studies how to extract innovative ideas for products from review data. To this end, we propose a deep learning-based method to extract sentences ... | [
"Representation Learning",
"Semantic Text Processing",
"Information Extraction & Text Mining"
] | [
12,
72,
3
] |
SCOPUS_ID:85065497244 | A Deep Learning-Based Named Entity Recognition in Biomedical Domain | In the biomedical field, huge amounts of data have been produced day by day. These data drives the development of the biomedical area researches in so many ways. This paper mainly focusing on biomedical named entity recognition (NER) with the aim to enhance the performance through deep learning. Impressive results in n... | [
"Named Entity Recognition",
"Information Extraction & Text Mining"
] | [
34,
3
] |
SCOPUS_ID:85128869983 | A Deep Learning-Based Sentiment Classification Model for Real Online Consumption | Most e-commerce platforms allow consumers to post product reviews, causing more and more consumers to get into the habit of reading reviews before they buy. These online reviews serve as an emotional feedback of consumers’ product experience and contain a lot of important information, but inevitably there are malicious... | [
"Information Extraction & Text Mining",
"Information Retrieval",
"Text Classification",
"Sentiment Analysis"
] | [
3,
24,
36,
78
] |
SCOPUS_ID:85128735112 | A Deep Learning-Based System for Document Layout Analysis | Document image understanding is an essential process in the digital transformation era. Those systems automatically convert a paper document to a digital document for storing and information extracting. In practice, document layout analysis is a critical step for the success of document image modeling. This paper intro... | [
"Visual Data in NLP",
"Multimodality"
] | [
20,
74
] |
SCOPUS_ID:85119331042 | A Deep Learning-based Approach for Emotions Classification in Big Corpus of Imbalanced Tweets | Emotions detection in natural languages is very effective in analyzing the user's mood about a concerned product, news, topic, and so on. However, it is really a challenging task to extract important features from a burst of raw social text, as emotions are subjective with limited fuzzy boundaries. These subjective fea... | [
"Text Classification",
"Sentiment Analysis",
"Emotion Analysis",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
36,
78,
61,
24,
3
] |
SCOPUS_ID:85146957972 | A Deep Learning-based Event Extraction Method in the Field of Electric Power Public Opinion | Event extraction is a sub-task of information extraction in natural language processing by extracting relevant event information from unstructured text. In order to obtain the hot events related to electric power public opinion in a timely manner and assist electric power staff to make quick decisions, this article sug... | [
"Event Extraction",
"Language Models",
"Semantic Text Processing",
"Information Extraction & Text Mining"
] | [
31,
52,
72,
3
] |
SCOPUS_ID:85143619946 | A Deep Learning-based Unified Solution for Character Recognition | Optical Character Recognition(OCR) has become a crucial area of research due to the vast number of digitized documents to lessen the dependency on paper. One can save time and money on data entry by automatically extracting information off paper and putting it where it needs to go. There has been much research on OCR s... | [
"Visual Data in NLP",
"Multimodality"
] | [
20,
74
] |
SCOPUS_ID:85090287910 | A Deep Level Tagger for Malayalam, a Morphologically Rich Language | In recent years, there has been tremendous growth in the amount of natural language text through various sources. Computational analysis of this text has got considerable attention among the NLP researchers. Automatic analysis and representation of natural language text is a step by step procedure. Deep level tagging i... | [
"Semantic Text Processing",
"Morphology",
"Syntactic Text Processing",
"Representation Learning",
"Tagging"
] | [
72,
73,
15,
12,
63
] |
http://arxiv.org/abs/1705.09975v1 | A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams | Cities have been a thriving place for citizens over the centuries due to their complex infrastructure. The emergence of the Cyber-Physical-Social Systems (CPSS) and context-aware technologies boost a growing interest in analysing, extracting and eventually understanding city events which subsequently can be utilised to... | [
"Event Extraction",
"Information Extraction & Text Mining"
] | [
31,
3
] |
SCOPUS_ID:85148038496 | A Deep Multi-level Attentive Network for Multimodal Sentiment Analysis | Multimodal sentiment analysis has attracted increasing attention with broad application prospects. Most of the existing methods have focused on a single modality, which fails to handle social media data due to its multiple modalities. Moreover, in multimodal learning, most of the works have focused on simply combining ... | [
"Visual Data in NLP",
"Information Extraction & Text Mining",
"Text Classification",
"Sentiment Analysis",
"Information Retrieval",
"Multimodality"
] | [
20,
3,
36,
78,
24,
74
] |
SCOPUS_ID:85083388164 | A Deep Multi-task Model for Dialogue Act Classification, Intent Detection and Slot Filling | An essential component of any dialogue system is understanding the language which is known as spoken language understanding (SLU). Dialogue act classification (DAC), intent detection (ID) and slot filling (SF) are significant aspects of every dialogue system. In this paper, we propose a deep learning-based multi-task m... | [
"Language Models",
"Low-Resource NLP",
"Semantic Text Processing",
"Information Retrieval",
"Information Extraction & Text Mining",
"Semantic Parsing",
"Sentiment Analysis",
"Intent Recognition",
"Natural Language Interfaces",
"Dialogue Systems & Conversational Agents",
"Text Classification",
... | [
52,
80,
72,
24,
3,
40,
78,
79,
11,
38,
36,
4
] |
SCOPUS_ID:85089219953 | A Deep Multimodal Approach for Map Image Classification | Map images (e.g., illustrated maps, historical maps, and geographic maps) have been published around the world, not only for giving location but also to attract tourists or hand down the histories of locations. The management of map data, however, has been an open issue for several research fields, including digital li... | [
"Visual Data in NLP",
"Text Classification",
"Multimodality",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
20,
36,
74,
24,
3
] |
SCOPUS_ID:85056125304 | A Deep Multiple View Sentence Representation Model for Question Answering | Question answering (QA) between humans and computers is regarded as one of the most hardcore problems in computer science, which involves interdisciplinary techniques in natural language processing. Existing deep models rely on a single sentence representation or multiple granularity representations for question answer... | [
"Language Models",
"Semantic Text Processing",
"Question Answering",
"Representation Learning",
"Natural Language Interfaces"
] | [
52,
72,
27,
12,
11
] |
SCOPUS_ID:85101216053 | A Deep Network Model for Paraphrase Detection in Punjabi | Paraphrase refers to the text which tells the same meanings but with different expressions. It is important in NLP as it deals with many applications such as information retrieval, information extraction, machine translation, query expansion, question answering, summarization and plagiarism. Paraphrase detection is to ... | [
"Paraphrasing",
"Semantic Text Processing",
"Green & Sustainable NLP",
"Representation Learning",
"Text Generation",
"Responsible & Trustworthy NLP"
] | [
32,
72,
68,
12,
47,
4
] |
http://arxiv.org/abs/1712.02820v1 | A Deep Network Model for Paraphrase Detection in Short Text Messages | This paper is concerned with paraphrase detection. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Given two sentences, the objective is to detect whe... | [
"Paraphrasing",
"Text Generation"
] | [
32,
47
] |
http://arxiv.org/abs/1707.01555v1 | A Deep Network with Visual Text Composition Behavior | While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a pi... | [
"Visual Data in NLP",
"Multimodality"
] | [
20,
74
] |
http://arxiv.org/abs/1706.08032v1 | A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking | This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for ... | [
"Semantic Text Processing",
"Text Classification",
"Representation Learning",
"Sentiment Analysis",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
72,
36,
12,
78,
24,
3
] |
http://arxiv.org/abs/2203.01594v1 | A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanism | Image captioning is a fast-growing research field of computer vision and natural language processing that involves creating text explanations for images. This study aims to develop a system that uses a pre-trained convolutional neural network (CNN) to extract features from an image, integrates the features with an atte... | [
"Visual Data in NLP",
"Language Models",
"Semantic Text Processing",
"Captioning",
"Text Generation",
"Multimodality"
] | [
20,
52,
72,
39,
47,
74
] |
http://arxiv.org/abs/1908.11057v2 | A Deep Neural Information Fusion Architecture for Textual Network Embeddings | Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations. Traditionally, the structural and textual embeddings were learned by models that rarely take the mutu... | [
"Semantic Text Processing",
"Representation Learning"
] | [
72,
12
] |
http://arxiv.org/abs/1709.09783v1 | A Deep Neural Network Approach To Parallel Sentence Extraction | Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. We propose an end-to-end deep neural network approach to detect translational equivalence between sentences in two different languages. In contrast to previous approaches, which ty... | [
"Multilinguality",
"Machine Translation",
"Text Generation",
"Information Extraction & Text Mining"
] | [
0,
51,
47,
3
] |
SCOPUS_ID:85107723151 | A Deep Neural Network Approach using Convolutional Network and Long Short Term Memory for Text Sentiment Classification | The current emotion-based text categorization method incorporates a lot of deep learning, such as LSTM (Long short term memory) and CNN (Convolutional neural network) algorithms. The traditional algorithm extracts relatively few text features, so the performance of the algorithm can be improved. Based on this fact, thi... | [
"Language Models",
"Semantic Text Processing",
"Text Classification",
"Sentiment Analysis",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
52,
72,
36,
78,
24,
3
] |
SCOPUS_ID:85077241235 | A Deep Neural Network Model for Joint Entity and Relation Extraction | Joint extraction of entities and their relations from the text is an essential issue in automatic knowledge graph construction, which is also known as the joint extraction of relational triplets. The relational triplets in sentence are complicated, multiple and different relational triplets may have overlaps, which is ... | [
"Semantic Text Processing",
"Relation Extraction",
"Structured Data in NLP",
"Knowledge Representation",
"Multimodality",
"Information Extraction & Text Mining"
] | [
72,
75,
50,
18,
74,
3
] |
SCOPUS_ID:85056528697 | A Deep Neural Network Model for Target-based Sentiment Analysis | In recent years, with the development of social networks, sentiment analysis has become one of the most important research topics in the field of natural language processing. The deep neural network model combining attention mechanism has achieved remarkable success in the task of target-based sentiment analysis. In cu... | [
"Language Models",
"Semantic Text Processing",
"Sentiment Analysis"
] | [
52,
72,
78
] |
SCOPUS_ID:85105446916 | A Deep Neural Network Model with Multihop Self-attention Mechanism for Topic Segmentation of Texts | Topic segmentation is an important task in the field of natural language processing (NLP), which finds its importance in applications such as information retrieval, text summarization, e-learning. Current neural methods for topic segmentation represent a sentence by a single feature vector that generates single semanti... | [
"Language Models",
"Text Segmentation",
"Semantic Text Processing",
"Syntactic Text Processing"
] | [
52,
21,
72,
15
] |
http://arxiv.org/abs/1809.00934v1 | A Deep Neural Network Sentence Level Classification Method with Context Information | In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a ... | [
"Language Models",
"Semantic Text Processing",
"Text Classification",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
52,
72,
36,
24,
3
] |
SCOPUS_ID:85134218653 | A Deep Neural Network-based Model for the Sentiment Analysis of Dravidian Code-mixed Social Media Posts | Sentiment analysis is one of the most essential jobs in natural language processing. The research community has recently presented a slew of papers aimed at detecting sentiment from English social media posts. Despite this, research on recognising feelings in Dravidian Kannada-English, Malayalam-English, and Tamil-Engl... | [
"Sentiment Analysis"
] | [
78
] |
SCOPUS_ID:85060036353 | A Deep Recurrent Neural Network with BiLSTM model for Sentiment Classification | In the field of sentiment classification, opinions or sentiments of the people are analyzed. Sentiment analysis systems are being applied in social platforms and in almost every business because the opinions or sentiments are the reflection of the beliefs, choices and activities of the people. With these systems it is ... | [
"Language Models",
"Semantic Text Processing",
"Text Classification",
"Sentiment Analysis",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
52,
72,
36,
78,
24,
3
] |
http://arxiv.org/abs/1705.04304v3 | A Deep Reinforced Model for Abstractive Summarization | Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that a... | [
"Summarization",
"Text Generation",
"Information Extraction & Text Mining"
] | [
30,
47,
3
] |
https://aclanthology.org//2020.ngt-1.7/ | A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards | Cross-lingual text summarization aims at generating a document summary in one language given input in another language. It is a practically important but under-explored task, primarily due to the dearth of available data. Existing methods resort to machine translation to synthesize training data, but such pipeline appr... | [
"Language Models",
"Low-Resource NLP",
"Machine Translation",
"Semantic Text Processing",
"Information Extraction & Text Mining",
"Semantic Similarity",
"Summarization",
"Text Generation",
"Responsible & Trustworthy NLP",
"Cross-Lingual Transfer",
"Multilinguality"
] | [
52,
80,
51,
72,
3,
53,
30,
47,
4,
19,
0
] |
http://arxiv.org/abs/1809.03118v1 | A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification | Multi-label text classification (MLTC) aims to assign multiple labels to each sample in the dataset. The labels usually have internal correlations. However, traditional methods tend to ignore the correlations between labels. In order to capture the correlations between labels, the sequence-to-sequence (Seq2Seq) model v... | [
"Language Models",
"Semantic Text Processing",
"Text Classification",
"Information Retrieval",
"Information Extraction & Text Mining"
] | [
52,
72,
36,
24,
3
] |
http://arxiv.org/abs/1709.02349v2 | A Deep Reinforcement Learning Chatbot | We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural langu... | [
"Natural Language Interfaces",
"Dialogue Systems & Conversational Agents"
] | [
11,
38
] |
http://arxiv.org/abs/1801.06700v1 | A Deep Reinforcement Learning Chatbot (Short Version) | We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural langu... | [
"Natural Language Interfaces",
"Dialogue Systems & Conversational Agents"
] | [
11,
38
] |
SCOPUS_ID:85076840283 | A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label | The classification of medical patents play an important role for pharmaceutical company, since medical patens with well labeled can significantly accelerate the process of new drug research. The previous studies using machine learning methods focus on classification the medical patents with single label. However, the c... | [
"Information Retrieval",
"Text Classification",
"Information Extraction & Text Mining"
] | [
24,
36,
3
] |
SCOPUS_ID:85103895843 | A Deep Semantic Alignment Network for the Cross-Modal Image-Text Retrieval in Remote Sensing | Because of the rapid growth of multimodal data from the internet and social media, a cross-modal retrieval has become an important and valuable task in recent years.The purpose of the cross-modal retrieval is to obtain the result data in one modality (e.g., image), which is semantically similar to the query data in ano... | [
"Visual Data in NLP",
"Captioning",
"Text Generation",
"Information Retrieval",
"Multimodality"
] | [
20,
39,
47,
24,
74
] |
http://arxiv.org/abs/1812.00176v1 | A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues | Discourse structures are beneficial for various NLP tasks such as dialogue understanding, question answering, sentiment analysis, and so on. This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues. The proposed model aims to construct a discourse dependency tree ... | [
"Semantic Text Processing",
"Semantic Parsing",
"Discourse & Pragmatics",
"Natural Language Interfaces",
"Dialogue Systems & Conversational Agents"
] | [
72,
40,
71,
11,
38
] |
SCOPUS_ID:85093838969 | A Deep Transfer Learning Approach for Fake News Detection | Fake or incorrect or miss-information detection has nowadays attracted attention to the researchers and developers because of the huge information overloaded in the web. This problem can be considered as equivalent to lie detection, truthfulness identification or stance detection. In our particular work, we focus on de... | [
"Language Models",
"Semantic Text Processing",
"Opinion Mining",
"Ethical NLP",
"Sentiment Analysis",
"Reasoning",
"Fact & Claim Verification",
"Textual Inference",
"Responsible & Trustworthy NLP"
] | [
52,
72,
49,
17,
78,
8,
46,
22,
4
] |
SCOPUS_ID:85144347580 | A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference | Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), has been one of the central tasks in Artificial Intelligence (AI) and Natural Language Processing (NLP). RTE between the two pieces of texts is a crucial problem, and it adds further challenges when involving two different languages, ... | [
"Language Models",
"Semantic Text Processing",
"Reasoning",
"Cross-Lingual Transfer",
"Textual Inference",
"Multilinguality"
] | [
52,
72,
8,
19,
22,
0
] |
SCOPUS_ID:85118180387 | A Deep Transfer Learning Method for Medical Question Matching | Question matching (QM) is a fundamental task of information retrieval (IR)-based question-answering (QA) systems, which can be formulated as a paraphrase identification (PI) problem and relies on large-scale labeled data, which is not easy to be obtained, especially in specific domains such as the medical domain. In th... | [
"Language Models",
"Paraphrasing",
"Semantic Text Processing",
"Question Answering",
"Natural Language Interfaces",
"Text Generation"
] | [
52,
32,
72,
27,
11,
47
] |
SCOPUS_ID:84969791928 | A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data | Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model c... | [
"Visual Data in NLP",
"Topic Modeling",
"Information Extraction & Text Mining",
"Multimodality"
] | [
20,
9,
3,
74
] |
SCOPUS_ID:85077207714 | A Deep learning approach for Arabic text classification | Advancement in information technology produced massive textual material that is available online. Text classification algorithms are at the core of many natural language processing (NLP) applications. There are several algorithms which have been implemented to tackle the classification problem for English and other Eur... | [
"Information Retrieval",
"Text Classification",
"Information Extraction & Text Mining"
] | [
24,
36,
3
] |
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