Massive text visualization is a burgeoning field that addresses the visualization, exploration, a... more Massive text visualization is a burgeoning field that addresses the visualization, exploration, and analysis of extensive textual datasets, encompassing various domains such as social media, scientific literature, news articles, and more. This paper overviews recent advances, challenges, and potential solutions in massive text visualization and systematically categorizes these works, focusing on shared characteristics such as objectives, preprocessing techniques, processing approaches, and visualization methods. This comprehensive analysis can better understand the current and emerging trends in massive text visualization.
Social media is an essential gateway of information and communication for people worldwide. The a... more Social media is an essential gateway of information and communication for people worldwide. The amount of time spent and reliance of people on social media makes it a vital resource for detecting events happening in real life. Thousands of significant events are posted by users every hour in the form of multimedia. Some individuals and groups target the audience to promote their agenda among these users. Their cause can threaten other groups and individuals who do not share the same views or have specific differences. Any group with a definitive cause cannot survive without the support which acts as a catalyst for their agenda. A phenomenon occurs where people are fed information that motivates them to act on their behalf and carry out their agenda. One is benefit results in the loss of the others by putting their lives, assets, physical and emotional health in danger. This paper introduces a concept of dangerous events to approach this problem and their three main types based on their characteristics: action, scenarios, and sentiment-based dangerous events.
Most importantly, none of this would have been possible without the love and patience of my famil... more Most importantly, none of this would have been possible without the love and patience of my family. My family to whom this dissertation is dedicated, has been a constant source of love, concern, support and strength for all these years. I would like to express my heartfelt gratitude to my wife and daughter-Carla Pereira and Lara Pais-who have helped and encouraged me. I also offer my deepest thanks and love to my mother-Maria Odete Pais-whose love and support has sustained me during these years. I cannot forget my father and my grandmother , who already left us, but I know both are looking to me, there, where they are… I thank you all!
Abstract Anaphora resolution is one of the problems in natural language processing. It is the pro... more Abstract Anaphora resolution is one of the problems in natural language processing. It is the process of disambiguating the antecedent of a referring expression from the set of entities in a discourse. The correct interpretation of pronouns plays an important role in the construction of meaning. Thus, the resolution of pronominal anaphors remains a very important task for many natural language processing applications. Additionally, it plays an increasingly significant role in computational linguistics. However, a significant amount of work on anaphora resolution is focused on English; anaphora resolution for other languages, including Arabic, is still limited. In this paper, we present a new set of computational and linguistic features to resolve Arabic anaphors using a machine learning approach. In this paper, an in-depth study was conducted on a set of computational and linguistic features to exploit their effectiveness and investigate their effect on anaphora resolution. The aim was to efficiently integrate different feature sets and classification algorithms to synthesize a more accurate classification procedure. Four well-known machine learning algorithms k-nearest neighbor, maximum entropy, decision tree and meta-classifier, were employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on Quran datasets was conducted, the discussion presented, and conclusions were drawn. The experimental results show that our approach gives satisfactory results.
Online social networking platforms allow people to freely express their ideas, opinions, and emot... more Online social networking platforms allow people to freely express their ideas, opinions, and emotions negatively or positively. Previous studies have examined sentiments on these platforms to study their behavior in different contexts and purposes. The mechanism of collecting public opinion information has attracted researchers to automatically classify the polarity of public opinions based on the use of concise language in messages, such as tweets, by analyzing social media data. In this paper, we extend the preceding work where an unsupervised approach to automatically detect extreme opinions/posts in social networks is proposed. The performance of the proposed approach is evaluated on five different social network and media datasets. In this work, we use a semi-supervised approach known as BERT to reevaluate the accuracy of our prior approach and the obtained classified dataset. The experiment proves that in these datasets, posts that were previously classified as negative or positive extreme are extremely negative or positive in many cases while using BERT. Furthermore, BERT shows the capability to classify the extreme sentiments when fine-tuned with an appropriate extreme sentiments dataset.
Nowadays, the use of language corpora for many purposes has increased significantly. General corp... more Nowadays, the use of language corpora for many purposes has increased significantly. General corpora exist for numerous languages, but research often needs more specialized corpora. The Web's rapid growth has significantly improved access to thousands of online documents, highly specialized texts and comparable texts on the same subject covering several languages in electronic form. However, research has continued to concentrate on corpus annotation instead of corpus creation tools. Consequently, many researchers create their corpora, independently solve problems, and generate project-specific systems. The corpus construction is used for many NLP applications, including machine translation, information retrieval, and question-answering. This paper presents a new NLP Corpus and Services in the Cloud called HULTIG-C. HULTIG-C is characterized by various languages that include unique annotations such as keywords set, sentences set, named entity recognition set, and multiword set. Moreover, a framework incorporates the main components for license detection, language identification, boilerplate removal and document deduplication to process the HULTIG-C. Furthermore, this paper presents some potential issues related to constructing multilingual corpora from the Web.
In this work, we present a new unsupervised and language-independent methodology to detect the re... more In this work, we present a new unsupervised and language-independent methodology to detect the relations of textual generality. For this, we introduce a particular case of Textual Entailment (TE), namely Textual Entailment by Generality (TEG). TE aims to capture primary semantic inference needs across applications in Natural Language Processing (NLP). Since 2005, in the TE Recognition (RTE) task, systems have been asked to automatically judge whether the meaning of a portion of the text, the Text (T), entails the meaning of another text, the Hypothesis (H). Several novel approaches and improvements in TE technologies demonstrated in RTE Challenges are signaling renewed interest towards a more in-depth and better understanding of the core phenomena involved in TE. In line with this direction, in this work, we focus on a particular case of entailment, entailment by generality, to detect the relations of textual generality. In text, there are different kinds of entailments, yielded from different types of implicative reasoning (lexical, syntactical, common sense based), but here, we focus just on TEG, which can be defined as an entailment from a specific statement towards a relatively more general one. Therefore, we have T G → H whenever the premise T entails the hypothesis H, this also being more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, from a pair T, H having an entailment relation. To this end, we introduce an Informative Asymmetric Measure (IAM) called Simplified Asymmetric InfoSimba (AISs), which we combine with different Asymmetric Association Measures (AAM). In this work, we hypothesize about the existence of a particular mode of TE, namely TEG. Thus, the main contribution of our study is highlighting the importance of this inference mechanism. Consequently, the new annotation data seem to be a valuable resource for the community.
HAL (Le Centre pour la Communication Scientifique Directe), 2011
In the context of Ephemeral Clustering of web Pages, it can be interesting to label each cluster ... more In the context of Ephemeral Clustering of web Pages, it can be interesting to label each cluster with a small summary instead of just a label. Within this scope, we introduce the paradigm of Textual Entailment by Generality, which can be defined as the entailment from a specific web snippet towards a more general web snippet. The subjacent idea is to find the best web snippet, which summarizes and subsumes all the other web snippets within an ephemeral cluster. To reach this objective, we first propose a new informative asymmetric similarity measure called the Simplified Asymmetric InfoSimba (AISs), which can be combined with different asymmetric association measures. In particular, the AISs proposes an unsupervised language-independent solution to infer Textual Entailment by Generality and as such can help to encounter the web snippet with maximum semantic coverage. This new methodology is tested against the first Recognizing Textual Entailment data set (RTE-1) 1 for an exhaustive number of asymmetric association measures with and without the identification of Multiword Units. The comparative experiments with existing state-of-the-art methodologies show promising results.
The shift in human computer interaction from desktop computing to mobile interaction highly influ... more The shift in human computer interaction from desktop computing to mobile interaction highly influences the needs for new designed interfaces. In this paper, we address the issue of searching for information on mobile devices, an area also known as Mobile Information Retrieval. In particular, we propose to summarize as much as possible the information retrieved by any search engine to allow universal access to information.
International Conference on Internet Monitoring and Protection, Sep 27, 2020
Social network providers usually describe the terms of data storage, usage, and sharing, by adopt... more Social network providers usually describe the terms of data storage, usage, and sharing, by adopting natural languages. To automatically evaluate such terms of use, to understand, analyse, and enforce rights and obligations over the user's data, it is of uttermost importance to translate them in a machine-readable format. Natural Languages (NLs) are the most prominent form of knowledge representation for humans. However, due to NLs complexities, it is quite burdensome to process their sentences by machines in a seamless and standardised way. Controlled Natural Languages (CNLs) are subsets of NLs that are obtained by restricting the grammar and vocabulary, to minimize-or even eliminate-ambiguity and complexity of NL. These languages hold two major characteristics: they look informal and easy to read and write by humans, quite like natural languages, but they can be easily transited into machine-readable forms. In this paper, we study some policy-oriented CNLs. We adopt them as source languages for translating sample Twitter policies. Then, we assess the value of the different languages, according to the difficulties of the translation, its readability, and other compelling properties to find which CNL is more suitable for NL translation.
Proceedings of the 30th ACM International Conference on Multimedia, Oct 10, 2022
Although a wide range of applications have been proposed in the field of multimodal natural langu... more Although a wide range of applications have been proposed in the field of multimodal natural language processing, very few works have been tackling multimodal relational lexical semantics. In this paper, we propose the first attempt to identify lexico-semantic relations with visual clues, which embody linguistic phenomena such as synonymy, co-hyponymy or hypernymy. While traditional methods take advantage of the paradigmatic approach or/and the distributional hypothesis, we hypothesize that visual information can supplement the textual information, relying on the apperceptum subcomponent of the semiotic textology linguistic theory. For that purpose, we automatically extend two gold-standard datasets with visual information, and develop different fusion techniques to combine textual and visual modalities following the patch-based strategy. Experimental results over the multimodal datasets show that the visual information can supplement the missing semantics of textual encodings with reliable performance improvements 1 .
The usability of the events information on social media has been widely studied recently. Several... more The usability of the events information on social media has been widely studied recently. Several surveys have reviewed the specific type of events on social media using various techniques. Most of the existing methods for event detection are segregated as they approach certain situations that limit the overall details of events happening consecutively on social media while ignoring the crucial relationship between the evolution of these events. Numerous events that materialize on the social media sphere every day before our eyes jeopardize people’s safety and are referred to by using a high-level concept of dangerous events. The front of dangerous events is broad, yet no known work exists that fully addresses and approaches this issue. This work introduces the term dangerous events and defines its scope in terms of practicality to establish the origins of the events caused by the previous events and their respective relationship. Furthermore, it divides dangerous events into sentim...
Natural language processing (NLP) refers to the field of study that focuses on the interactions b... more Natural language processing (NLP) refers to the field of study that focuses on the interactions between human language and computers. It has recently gained much attention for analyzing human language computationally and has spread its applications for various tasks such as machine translation, information extraction, summarization, question answering, and others. With the rapid growth of cloud computing services, merging NLP in the cloud is a significant benefit. It allows researchers to conduct NLP-related experiments on large amounts of data handled by big data techniques while harnessing the cloud’s vast, on-demand computing power. However, it has not sufficiently spread its tools and applications as a service in the cloud and there is little literature available that discusses the scope of interdisciplinary work. NLP, cloud Computing, and big data are vast domains and contain their challenges and potentials. By overcoming those challenges and integrating these fields, great pot...
Proceedings of the 30th ACM International Conference on Multimedia
Although a wide range of applications have been proposed in the field of multimodal natural langu... more Although a wide range of applications have been proposed in the field of multimodal natural language processing, very few works have been tackling multimodal relational lexical semantics. In this paper, we propose the first attempt to identify lexico-semantic relations with visual clues, which embody linguistic phenomena such as synonymy, co-hyponymy or hypernymy. While traditional methods take advantage of the paradigmatic approach or/and the distributional hypothesis, we hypothesize that visual information can supplement the textual information, relying on the apperceptum subcomponent of the semiotic textology linguistic theory. For that purpose, we automatically extend two gold-standard datasets with visual information, and develop different fusion techniques to combine textual and visual modalities following the patch-based strategy. Experimental results over the multimodal datasets show that the visual information can supplement the missing semantics of textual encodings with reliable performance improvements 1 .
Massive text visualization is a burgeoning field that addresses the visualization, exploration, a... more Massive text visualization is a burgeoning field that addresses the visualization, exploration, and analysis of extensive textual datasets, encompassing various domains such as social media, scientific literature, news articles, and more. This paper overviews recent advances, challenges, and potential solutions in massive text visualization and systematically categorizes these works, focusing on shared characteristics such as objectives, preprocessing techniques, processing approaches, and visualization methods. This comprehensive analysis can better understand the current and emerging trends in massive text visualization.
Social media is an essential gateway of information and communication for people worldwide. The a... more Social media is an essential gateway of information and communication for people worldwide. The amount of time spent and reliance of people on social media makes it a vital resource for detecting events happening in real life. Thousands of significant events are posted by users every hour in the form of multimedia. Some individuals and groups target the audience to promote their agenda among these users. Their cause can threaten other groups and individuals who do not share the same views or have specific differences. Any group with a definitive cause cannot survive without the support which acts as a catalyst for their agenda. A phenomenon occurs where people are fed information that motivates them to act on their behalf and carry out their agenda. One is benefit results in the loss of the others by putting their lives, assets, physical and emotional health in danger. This paper introduces a concept of dangerous events to approach this problem and their three main types based on their characteristics: action, scenarios, and sentiment-based dangerous events.
Most importantly, none of this would have been possible without the love and patience of my famil... more Most importantly, none of this would have been possible without the love and patience of my family. My family to whom this dissertation is dedicated, has been a constant source of love, concern, support and strength for all these years. I would like to express my heartfelt gratitude to my wife and daughter-Carla Pereira and Lara Pais-who have helped and encouraged me. I also offer my deepest thanks and love to my mother-Maria Odete Pais-whose love and support has sustained me during these years. I cannot forget my father and my grandmother , who already left us, but I know both are looking to me, there, where they are… I thank you all!
Abstract Anaphora resolution is one of the problems in natural language processing. It is the pro... more Abstract Anaphora resolution is one of the problems in natural language processing. It is the process of disambiguating the antecedent of a referring expression from the set of entities in a discourse. The correct interpretation of pronouns plays an important role in the construction of meaning. Thus, the resolution of pronominal anaphors remains a very important task for many natural language processing applications. Additionally, it plays an increasingly significant role in computational linguistics. However, a significant amount of work on anaphora resolution is focused on English; anaphora resolution for other languages, including Arabic, is still limited. In this paper, we present a new set of computational and linguistic features to resolve Arabic anaphors using a machine learning approach. In this paper, an in-depth study was conducted on a set of computational and linguistic features to exploit their effectiveness and investigate their effect on anaphora resolution. The aim was to efficiently integrate different feature sets and classification algorithms to synthesize a more accurate classification procedure. Four well-known machine learning algorithms k-nearest neighbor, maximum entropy, decision tree and meta-classifier, were employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on Quran datasets was conducted, the discussion presented, and conclusions were drawn. The experimental results show that our approach gives satisfactory results.
Online social networking platforms allow people to freely express their ideas, opinions, and emot... more Online social networking platforms allow people to freely express their ideas, opinions, and emotions negatively or positively. Previous studies have examined sentiments on these platforms to study their behavior in different contexts and purposes. The mechanism of collecting public opinion information has attracted researchers to automatically classify the polarity of public opinions based on the use of concise language in messages, such as tweets, by analyzing social media data. In this paper, we extend the preceding work where an unsupervised approach to automatically detect extreme opinions/posts in social networks is proposed. The performance of the proposed approach is evaluated on five different social network and media datasets. In this work, we use a semi-supervised approach known as BERT to reevaluate the accuracy of our prior approach and the obtained classified dataset. The experiment proves that in these datasets, posts that were previously classified as negative or positive extreme are extremely negative or positive in many cases while using BERT. Furthermore, BERT shows the capability to classify the extreme sentiments when fine-tuned with an appropriate extreme sentiments dataset.
Nowadays, the use of language corpora for many purposes has increased significantly. General corp... more Nowadays, the use of language corpora for many purposes has increased significantly. General corpora exist for numerous languages, but research often needs more specialized corpora. The Web's rapid growth has significantly improved access to thousands of online documents, highly specialized texts and comparable texts on the same subject covering several languages in electronic form. However, research has continued to concentrate on corpus annotation instead of corpus creation tools. Consequently, many researchers create their corpora, independently solve problems, and generate project-specific systems. The corpus construction is used for many NLP applications, including machine translation, information retrieval, and question-answering. This paper presents a new NLP Corpus and Services in the Cloud called HULTIG-C. HULTIG-C is characterized by various languages that include unique annotations such as keywords set, sentences set, named entity recognition set, and multiword set. Moreover, a framework incorporates the main components for license detection, language identification, boilerplate removal and document deduplication to process the HULTIG-C. Furthermore, this paper presents some potential issues related to constructing multilingual corpora from the Web.
In this work, we present a new unsupervised and language-independent methodology to detect the re... more In this work, we present a new unsupervised and language-independent methodology to detect the relations of textual generality. For this, we introduce a particular case of Textual Entailment (TE), namely Textual Entailment by Generality (TEG). TE aims to capture primary semantic inference needs across applications in Natural Language Processing (NLP). Since 2005, in the TE Recognition (RTE) task, systems have been asked to automatically judge whether the meaning of a portion of the text, the Text (T), entails the meaning of another text, the Hypothesis (H). Several novel approaches and improvements in TE technologies demonstrated in RTE Challenges are signaling renewed interest towards a more in-depth and better understanding of the core phenomena involved in TE. In line with this direction, in this work, we focus on a particular case of entailment, entailment by generality, to detect the relations of textual generality. In text, there are different kinds of entailments, yielded from different types of implicative reasoning (lexical, syntactical, common sense based), but here, we focus just on TEG, which can be defined as an entailment from a specific statement towards a relatively more general one. Therefore, we have T G → H whenever the premise T entails the hypothesis H, this also being more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, from a pair T, H having an entailment relation. To this end, we introduce an Informative Asymmetric Measure (IAM) called Simplified Asymmetric InfoSimba (AISs), which we combine with different Asymmetric Association Measures (AAM). In this work, we hypothesize about the existence of a particular mode of TE, namely TEG. Thus, the main contribution of our study is highlighting the importance of this inference mechanism. Consequently, the new annotation data seem to be a valuable resource for the community.
HAL (Le Centre pour la Communication Scientifique Directe), 2011
In the context of Ephemeral Clustering of web Pages, it can be interesting to label each cluster ... more In the context of Ephemeral Clustering of web Pages, it can be interesting to label each cluster with a small summary instead of just a label. Within this scope, we introduce the paradigm of Textual Entailment by Generality, which can be defined as the entailment from a specific web snippet towards a more general web snippet. The subjacent idea is to find the best web snippet, which summarizes and subsumes all the other web snippets within an ephemeral cluster. To reach this objective, we first propose a new informative asymmetric similarity measure called the Simplified Asymmetric InfoSimba (AISs), which can be combined with different asymmetric association measures. In particular, the AISs proposes an unsupervised language-independent solution to infer Textual Entailment by Generality and as such can help to encounter the web snippet with maximum semantic coverage. This new methodology is tested against the first Recognizing Textual Entailment data set (RTE-1) 1 for an exhaustive number of asymmetric association measures with and without the identification of Multiword Units. The comparative experiments with existing state-of-the-art methodologies show promising results.
The shift in human computer interaction from desktop computing to mobile interaction highly influ... more The shift in human computer interaction from desktop computing to mobile interaction highly influences the needs for new designed interfaces. In this paper, we address the issue of searching for information on mobile devices, an area also known as Mobile Information Retrieval. In particular, we propose to summarize as much as possible the information retrieved by any search engine to allow universal access to information.
International Conference on Internet Monitoring and Protection, Sep 27, 2020
Social network providers usually describe the terms of data storage, usage, and sharing, by adopt... more Social network providers usually describe the terms of data storage, usage, and sharing, by adopting natural languages. To automatically evaluate such terms of use, to understand, analyse, and enforce rights and obligations over the user's data, it is of uttermost importance to translate them in a machine-readable format. Natural Languages (NLs) are the most prominent form of knowledge representation for humans. However, due to NLs complexities, it is quite burdensome to process their sentences by machines in a seamless and standardised way. Controlled Natural Languages (CNLs) are subsets of NLs that are obtained by restricting the grammar and vocabulary, to minimize-or even eliminate-ambiguity and complexity of NL. These languages hold two major characteristics: they look informal and easy to read and write by humans, quite like natural languages, but they can be easily transited into machine-readable forms. In this paper, we study some policy-oriented CNLs. We adopt them as source languages for translating sample Twitter policies. Then, we assess the value of the different languages, according to the difficulties of the translation, its readability, and other compelling properties to find which CNL is more suitable for NL translation.
Proceedings of the 30th ACM International Conference on Multimedia, Oct 10, 2022
Although a wide range of applications have been proposed in the field of multimodal natural langu... more Although a wide range of applications have been proposed in the field of multimodal natural language processing, very few works have been tackling multimodal relational lexical semantics. In this paper, we propose the first attempt to identify lexico-semantic relations with visual clues, which embody linguistic phenomena such as synonymy, co-hyponymy or hypernymy. While traditional methods take advantage of the paradigmatic approach or/and the distributional hypothesis, we hypothesize that visual information can supplement the textual information, relying on the apperceptum subcomponent of the semiotic textology linguistic theory. For that purpose, we automatically extend two gold-standard datasets with visual information, and develop different fusion techniques to combine textual and visual modalities following the patch-based strategy. Experimental results over the multimodal datasets show that the visual information can supplement the missing semantics of textual encodings with reliable performance improvements 1 .
The usability of the events information on social media has been widely studied recently. Several... more The usability of the events information on social media has been widely studied recently. Several surveys have reviewed the specific type of events on social media using various techniques. Most of the existing methods for event detection are segregated as they approach certain situations that limit the overall details of events happening consecutively on social media while ignoring the crucial relationship between the evolution of these events. Numerous events that materialize on the social media sphere every day before our eyes jeopardize people’s safety and are referred to by using a high-level concept of dangerous events. The front of dangerous events is broad, yet no known work exists that fully addresses and approaches this issue. This work introduces the term dangerous events and defines its scope in terms of practicality to establish the origins of the events caused by the previous events and their respective relationship. Furthermore, it divides dangerous events into sentim...
Natural language processing (NLP) refers to the field of study that focuses on the interactions b... more Natural language processing (NLP) refers to the field of study that focuses on the interactions between human language and computers. It has recently gained much attention for analyzing human language computationally and has spread its applications for various tasks such as machine translation, information extraction, summarization, question answering, and others. With the rapid growth of cloud computing services, merging NLP in the cloud is a significant benefit. It allows researchers to conduct NLP-related experiments on large amounts of data handled by big data techniques while harnessing the cloud’s vast, on-demand computing power. However, it has not sufficiently spread its tools and applications as a service in the cloud and there is little literature available that discusses the scope of interdisciplinary work. NLP, cloud Computing, and big data are vast domains and contain their challenges and potentials. By overcoming those challenges and integrating these fields, great pot...
Proceedings of the 30th ACM International Conference on Multimedia
Although a wide range of applications have been proposed in the field of multimodal natural langu... more Although a wide range of applications have been proposed in the field of multimodal natural language processing, very few works have been tackling multimodal relational lexical semantics. In this paper, we propose the first attempt to identify lexico-semantic relations with visual clues, which embody linguistic phenomena such as synonymy, co-hyponymy or hypernymy. While traditional methods take advantage of the paradigmatic approach or/and the distributional hypothesis, we hypothesize that visual information can supplement the textual information, relying on the apperceptum subcomponent of the semiotic textology linguistic theory. For that purpose, we automatically extend two gold-standard datasets with visual information, and develop different fusion techniques to combine textual and visual modalities following the patch-based strategy. Experimental results over the multimodal datasets show that the visual information can supplement the missing semantics of textual encodings with reliable performance improvements 1 .
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