Chatbot is a conversational agent that communicates with users based on natural language. It is f... more Chatbot is a conversational agent that communicates with users based on natural language. It is founded on a question answering system which tries to understand the intent of the user. Several chatbot methods deal with a model based template of question answering. However, these approaches are not able to cope with various questions and can affect the quality of the results. To address this issue, we propose a new semantic question answering approach combining Natural Language Processing (NLP) methods and Semantic Web techniques to analyze user’s question and transform it into SPARQL query. An ontology has been developed to represent the domain knowledge of the chatbot. Experimentations show that our approach outperforms state of the art methods.
Chatbot is a conversational agent that communicates with users based on natural language. It is f... more Chatbot is a conversational agent that communicates with users based on natural language. It is founded on a question answering system which tries to understand the intent of the user. Several chatbot methods deal with a model based template of question answering. However, these approaches are not able to cope with various questions and can affect the quality of the results. To address this issue, we propose a new semantic question answering approach combining Natural Language Processing (NLP) methods and Semantic Web techniques to analyze user’s question and transform it into SPARQL query. An ontology has been developed to represent the domain knowledge of the chatbot. Experimentations show that our approach outperforms state of the art methods.
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
The overflowing of textual data on the web needs an efficient tool that is able to manage and pro... more The overflowing of textual data on the web needs an efficient tool that is able to manage and process data. In this context, automatic text summarization has shown a great importance in several application areas. It aims to create a coherent and fluent short version of a document while preserving of the main information. This method allows for a reduction in reading time by condensing relevant information from a large collection of documents. Several automatic text summarization approaches have been proposed in order to entail shorten parts of the document. These methods have good results, but they still need improvements related to the reliability of sentences extraction, redundancy, semantic relationships between sentences, etc. This paper introduces a new hybrid architecture, combining a 2-layer recurrent neural network (RNN) extractive model and a sequence-to-sequence attentional abstractive model. This method uses the advantages of both extractive and abstractive approaches. A ...
Textual data are available in large unstructured volumes. Processing this data is becoming crucia... more Textual data are available in large unstructured volumes. Processing this data is becoming crucial and document classification is a way of structuring and processing this information based on its content. This paper introduces an effective semantic text mining approach for document classification. The proposed approach Semantic Enriched Deep Learning Architecture (SE-DLA) allows the model to learn simultaneously from the generated semantic vector representations and the original document vectors. We evaluated the proposed method on topic categorizations and multi-label classification. The experiments demonstrate that the proposed hybrid architecture with the additional semantic knowledge improves the results. This approach was compared to some state-of-the-art text classification approaches not including semantic knowledge. The proposed SE-DLA achieved higher accuracy and maintained great results during the experimental process.
Text document representation is one of the main issue in the text analysis areas such as topic ex... more Text document representation is one of the main issue in the text analysis areas such as topic extraction and text similarities. Standard Bag-of-Word representation does not deal with relationships between words. In order to overcome this limitation, we introduce a new approach based on the joint use of co-occurrence graph and semantic network of English language called Wordnet. To do this, a word sense disambiguation algorithm has been used in order to establish semantic links between terms given the surrounding context. Experimentations on standard datasets show good performances of the proposed approach. MOTS-CLÉS : Représentation des textes, WordNet, graphe, désambiguïsation des mots, sémantique.
Text detection and recognition have witnessed drastic improvements in the field of computer visio... more Text detection and recognition have witnessed drastic improvements in the field of computer vision. This end-toend model comprising of the detection and recognition models scales to provide higher accuracy. The most important phase in this end-to-end approach is the detection phase, as it plays an important role to identify the text. To address this issue, different approaches have been proposed. However, most of the methods produce lower efficiency to detect and recognize real world text. In this paper, we propose a new approach to investigate the challenges that the existing models possess and improve the efficiency of the detection and in turn increases the accuracy of text recognition. The proposed method outperforms the state-ofthe-art approaches due to the use of deblurring and sharpening to reduce noise in the pre-processing stage, followed by the cascade region proposal network model to improve the detection of real world text using non max suppression. Experimentations on r...
Résumé. Le chatbot est un agent conversationnel qui communique avec les utilisateurs en langage n... more Résumé. Le chatbot est un agent conversationnel qui communique avec les utilisateurs en langage naturel. Il est fondé sur un système de questions-réponses, les questions traitant l’intention de l’utilisateur. Dans ce contexte, des travaux récents ont été abordés présentant certaines limites. L’originalité de notre approche consiste à combiner les méthodes de traitement automatique du langage naturel avec les techniques du web sémantique. Une ontologie de domaine sert de base de connaissances pour décrire les informations dans un triplestore RDF. Les premiers résultats expérimentaux montrent l’intérêt de nos propositions.
Chatbot is a conversational agent that communicates with users based on natural language. It is f... more Chatbot is a conversational agent that communicates with users based on natural language. It is founded on a question answering system which tries to understand the intent of the user. Several chatbot methods deal with a model based template of question answering. However, these approaches are not able to cope with various questions and can affect the quality of the results. To address this issue, we propose a new semantic question answering approach combining Natural Language Processing (NLP) methods and Semantic Web techniques to analyze user's question and transform it into SPARQL query. An ontology has been developed to represent the domain knowledge of the chatbot. Experimentations show that our approach outperforms state of the art methods.
Chatbot is a conversational agent that communicates with users based on natural language. It is f... more Chatbot is a conversational agent that communicates with users based on natural language. It is founded on a question answering system which tries to understand the intent of the user. Several chatbot methods deal with a model based template of question answering. However, these approaches are not able to cope with various questions and can affect the quality of the results. To address this issue, we propose a new semantic question answering approach combining Natural Language Processing (NLP) methods and Semantic Web techniques to analyze user’s question and transform it into SPARQL query. An ontology has been developed to represent the domain knowledge of the chatbot. Experimentations show that our approach outperforms state of the art methods.
Chatbot is a conversational agent that communicates with users based on natural language. It is f... more Chatbot is a conversational agent that communicates with users based on natural language. It is founded on a question answering system which tries to understand the intent of the user. Several chatbot methods deal with a model based template of question answering. However, these approaches are not able to cope with various questions and can affect the quality of the results. To address this issue, we propose a new semantic question answering approach combining Natural Language Processing (NLP) methods and Semantic Web techniques to analyze user’s question and transform it into SPARQL query. An ontology has been developed to represent the domain knowledge of the chatbot. Experimentations show that our approach outperforms state of the art methods.
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
The overflowing of textual data on the web needs an efficient tool that is able to manage and pro... more The overflowing of textual data on the web needs an efficient tool that is able to manage and process data. In this context, automatic text summarization has shown a great importance in several application areas. It aims to create a coherent and fluent short version of a document while preserving of the main information. This method allows for a reduction in reading time by condensing relevant information from a large collection of documents. Several automatic text summarization approaches have been proposed in order to entail shorten parts of the document. These methods have good results, but they still need improvements related to the reliability of sentences extraction, redundancy, semantic relationships between sentences, etc. This paper introduces a new hybrid architecture, combining a 2-layer recurrent neural network (RNN) extractive model and a sequence-to-sequence attentional abstractive model. This method uses the advantages of both extractive and abstractive approaches. A ...
Textual data are available in large unstructured volumes. Processing this data is becoming crucia... more Textual data are available in large unstructured volumes. Processing this data is becoming crucial and document classification is a way of structuring and processing this information based on its content. This paper introduces an effective semantic text mining approach for document classification. The proposed approach Semantic Enriched Deep Learning Architecture (SE-DLA) allows the model to learn simultaneously from the generated semantic vector representations and the original document vectors. We evaluated the proposed method on topic categorizations and multi-label classification. The experiments demonstrate that the proposed hybrid architecture with the additional semantic knowledge improves the results. This approach was compared to some state-of-the-art text classification approaches not including semantic knowledge. The proposed SE-DLA achieved higher accuracy and maintained great results during the experimental process.
Text document representation is one of the main issue in the text analysis areas such as topic ex... more Text document representation is one of the main issue in the text analysis areas such as topic extraction and text similarities. Standard Bag-of-Word representation does not deal with relationships between words. In order to overcome this limitation, we introduce a new approach based on the joint use of co-occurrence graph and semantic network of English language called Wordnet. To do this, a word sense disambiguation algorithm has been used in order to establish semantic links between terms given the surrounding context. Experimentations on standard datasets show good performances of the proposed approach. MOTS-CLÉS : Représentation des textes, WordNet, graphe, désambiguïsation des mots, sémantique.
Text detection and recognition have witnessed drastic improvements in the field of computer visio... more Text detection and recognition have witnessed drastic improvements in the field of computer vision. This end-toend model comprising of the detection and recognition models scales to provide higher accuracy. The most important phase in this end-to-end approach is the detection phase, as it plays an important role to identify the text. To address this issue, different approaches have been proposed. However, most of the methods produce lower efficiency to detect and recognize real world text. In this paper, we propose a new approach to investigate the challenges that the existing models possess and improve the efficiency of the detection and in turn increases the accuracy of text recognition. The proposed method outperforms the state-ofthe-art approaches due to the use of deblurring and sharpening to reduce noise in the pre-processing stage, followed by the cascade region proposal network model to improve the detection of real world text using non max suppression. Experimentations on r...
Résumé. Le chatbot est un agent conversationnel qui communique avec les utilisateurs en langage n... more Résumé. Le chatbot est un agent conversationnel qui communique avec les utilisateurs en langage naturel. Il est fondé sur un système de questions-réponses, les questions traitant l’intention de l’utilisateur. Dans ce contexte, des travaux récents ont été abordés présentant certaines limites. L’originalité de notre approche consiste à combiner les méthodes de traitement automatique du langage naturel avec les techniques du web sémantique. Une ontologie de domaine sert de base de connaissances pour décrire les informations dans un triplestore RDF. Les premiers résultats expérimentaux montrent l’intérêt de nos propositions.
Chatbot is a conversational agent that communicates with users based on natural language. It is f... more Chatbot is a conversational agent that communicates with users based on natural language. It is founded on a question answering system which tries to understand the intent of the user. Several chatbot methods deal with a model based template of question answering. However, these approaches are not able to cope with various questions and can affect the quality of the results. To address this issue, we propose a new semantic question answering approach combining Natural Language Processing (NLP) methods and Semantic Web techniques to analyze user's question and transform it into SPARQL query. An ontology has been developed to represent the domain knowledge of the chatbot. Experimentations show that our approach outperforms state of the art methods.
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