lobna hlaoua

Followers

Following

Co-authors

Public Views

Interests

Uploads

Papers by lobna hlaoua

Fuzzy Twin SVM based-Profile Categorization approach

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2018

Through the enormous impact of social media on our daily life, an important information database ... more Through the enormous impact of social media on our daily life, an important information database can be obtained from theses medias. But we have a lack of information about those who create the contents. The aim of author profiling is to analyze authors published texts in order to determine theirs profile category. To solve and optimize the multi-class categorization problem we propose in this paper a profile categorization approach. Our approach divided into processing module and the classifying module. Firstly we are going to adopt TF-IDF with threshold filtering method during the feature extraction step. Then we incorporate the fuzzy set theory into Twin SVM (TSVM), then OAA-TSVM (One-Against-All TSVM) OAO-TSVM(One-Against-One TSVM) are used in the classifying module. Our proposed classifier are tested using the RepLab 2014 Data set and performance measures: precision, recall and F-measure are used for the evaluation. Result obtained show that our proposed profile categorization approach perform very well.

A Deep Learning-based Ranking Approach for Microblog Retrieval

Procedia Computer Science, 2019

Today, Twitter has become one of the most popular micro-blogging service with a large amount of i... more Today, Twitter has become one of the most popular micro-blogging service with a large amount of information on various topics produced by millions of users every day. When searching for useful information in Twitter, users need to assess high quality content that meets their needs. However, the study of effective information retrieval in such microblog is still a challenge because there is a large difference in the quality level of relevant tweets returned in the search results for a given query. Therefore, looking for an effective microblog retrieval requires distinguishing the high-quality tweet content among thousands of results. Existing works are based on hand-crafted features (e.g., number of re-tweets, number of followers, etc.) using hard-hand-engineering to indicate the quality of tweets. In this paper, we focus on the problem of ranking tweets, and particularly on retrieving high quality content. We propose a ranking approach based on k-means clustering to distinguish high quality from low quality tweets. The clustering algorithm is based on learning features from deep learning autoencoder and hand-crafted features from tweets' content and authors' profiles. We used information gain as a feature importance measure to find the optimal set of features having stronger power in clustering the data. By conducting a pilot feature analysis study, we demonstrate the impact of the learned features to identify tweets' quality in the clustering process. Our experimental results show that the integration of learned features has shown significant improvement in the quality of clustering and especially on the ranking performance compared to the use of hand-crafted features only.

Download

F-31062 Toulouse Cedex9

Relevance Feedback (RF) is a technique allowing to enrich an initial query according to the user ... more Relevance Feedback (RF) is a technique allowing to enrich an initial query according to the user feedback. The goal is to express more precisily the user’s needs. Some open issues appear when considering semi-structured documents like XML documents. Most of the RF approaches proposed in XML retrieval are simple adaptations of traditional RF to the new granularity of information. They enrich queries by adding terms extracted from relevant elements instead of terms extracted from whole documents. In this paper we show how structural constraints can also be used in RF. We propose a new approach that is able to extend the initial query by adding one or more generative structures. This approach is applied to unstructured queries. Experiments are carried out on INEX collection and results show the interest of our method. Categories and Subject Descriptors H.3.3 [Information storage and retrieval]: Information search and retrieval—retrieval models; H.3.4 [Information storage and retrieval]...

Uncertain Confidence Network-Based Collaborative Information Retrieval Relevance Feedback Algorithm

Collaborative retrieval allows increasing the amount of relevant information found and sharing hi... more Collaborative retrieval allows increasing the amount of relevant information found and sharing history with others. The collaborative retrieval can reduce the retrieval time performed by the users of the same profile. This chapter proposes a new relevance feedback algorithm to collaborative information retrieval based on a confidence network, which performs propagation relevance between annotations terms. The main contribution in this work is the extraction of relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships estimated to the measure of a confidence. Since the user is overwhelmed by a variety of contradictory annotations, another contribution consists of determining the relevant annotations for a given evidence source. The experimental study gives very encouraging results. Uncertain Confidence Net...

Download

Improving Readability for Tweet Contextualization using Bipartite Graphs

Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2019

Tweet contextualization (TC) is a new issue that aims to answer questions of the form What is thi... more Tweet contextualization (TC) is a new issue that aims to answer questions of the form What is this tweet about? The idea of this task was imagined as an extension of a previous area called multi-document summarization (MDS), which consists in generating a summary from many sources. In both TC and MDS, the summary should ideally contain most relevant information of the topic that is being discussed in the source texts (for MDS) and related to the query (for TC). Furthermore of being informative, a summary should be coherent, i.e. well written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach to improve readability and coherence for tweet contextualization based on bipartite graphs. The main idea of our proposed method is to reorder sentences in a given paragraph by combining most expressive words detection and HITS (Hyperlink-Induced Topic Search) algorithm to make up a coherent context.

Download

Collaborative information retrieval model based on fuzzy confidence network

Journal of Intelligent & Fuzzy Systems, 2016

The Web information is too heterogeneous that users have difficulties to retrieve their needed in... more The Web information is too heterogeneous that users have difficulties to retrieve their needed information: text, image or video. Indeed, the collaborative work presents one solution proposed to solve this problem. Collaborative retrieval enables the retrieval histories' sharing between users having the same profile across multiple tools such as annotations. However the user has always problems in choosing the terms to form his query. This paper has proposed collaborative Information Retrieval Model based on Fuzzy Confidence Network. Our approach allows the detection of relevant annotations to a given evidence source. These annotations are next filtered to determine which are relevant to consider them as a new source of information that describes the document used to improve collaborative retrieval performance. We then measure the semantic relationships between terms which will be translated by the propagation of confidence. Experiments were conducted on different queries, showing very encouraging results that could reach an improvement rate.

Download

G-Form: A New Approach for Visual Interpretation of Deep Web Form as Galaxy of Concepts

Deep Web is growing rapidly with multitude of devices and rendering capabilities. Despite the ric... more Deep Web is growing rapidly with multitude of devices and rendering capabilities. Despite the richness of Deep Web forms, their rendering methodology is very poor in terms of capacity of expression. Hence, the user has no indication about the richness of the query and the query capability when he interprets this interface. In this paper, we propose a new rendering approach of Deep Web forms which is easy to interpret by the user and reflects the exact meaning of the query. We have evaluated our algorithm on standard dataset and compared it to a well known state of the art algorithm. Our approach showed good performance with respect to standard measures.

Download

Using a Content-and-Structure Oriented Method for Relevance Feedback in XML Retrieval

As opposed to traditional Information Retrieval (IR) which views whole documents as atomic units ... more As opposed to traditional Information Retrieval (IR) which views whole documents as atomic units of retrieval, XML IR processes XML elements as possible units of retrieval. Many open issues appear when considering Relevance Feedback (RF) in XML documents. They are mainly related to the form of XML documents that mix content and structure and to the new granularity of information processed by the Information Retrieval Systems (IRS). Most of the RF approaches proposed in XML retrieval are simple adaptations of traditional RF to the new granularity of information. They enrich queries by adding terms extracted from relevant elements instead of terms extracted from whole documents. In this paper, we propose to extend the initial query by adding both content and structural constraints. Experiments are carried out with the INEX evaluation campaign and results show the interest of our method.

Download

An automatic learning for re-ranking in social information retrieval

2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA

A Social Information Retrieval (SIR) result re-ranking system offers several techniques and metho... more A Social Information Retrieval (SIR) result re-ranking system offers several techniques and methods that meet the need of the user to express through a given query and return a re-ranked list of returned results. This list should contains the information relevant to the top ranking since the majority of these users are only interested in the first results displayed. However, optimizing the returned results ranking becomes a fundamental problem when searching for tweets. In this regard, this paper presents a re-ranking model of social retrieval results based on automatic learning. Indeed, this model can re-rank the list of results obtained by the SIR system based on adaptive features given query category. Also, our model devises queries into categories and learn the representative features of each. More precisely, in order to reorder the result obtained from a given query, our model allows recalculating a new relevance score for each corresponding tweet. To validate this proposed approach, the experiments were carried out based on queries from different categories. The results obtained from his experiments show the effectiveness of our model compared to classic ranking approaches.

CLIQUES DETECTION vs MAXIMUM SPANNING TREE FOR TWEET CONTEXTUALIZATION

Download

Profile Categorization System based on Features Reduction

Though the enormous impact of social media on our daily life, we observe a lack of information ab... more Though the enormous impact of social media on our daily life, we observe a lack of information about those who create the contents. In this regard, author profiling tries to determine the profile category of authors by analysing their published texts. This paper presents a profile categorization system to solve the multi-class categorization problem. The system consists of two modules: the processing module and the classifying module. In the first module TF-IDF with threshold filtering method are used to extract the relevant terms. While support vector machine (SVM) is used in the classifying module. Our proposed feature reduction method is tested using the RepLab 2014 Data set and evaluated using performance measures: precision, recall and F-measure. Result obtained show that proposed method improves system performance when solving a multi-class profile categorization problem.

Tweet contextualization: combining sentence extraction, sentence aggregation and sentence reordering to enhance informativeness and readability

Social Network Analysis and Mining

Nowadays, social medias are very popular among their users. One of the most well-known social net... more Nowadays, social medias are very popular among their users. One of the most well-known social networks is Twitter. It is a micro-blog that enables its users to send short messages called tweets. A tweet is a 280 characters long message that is rarely self-content. Hence, additional information is necessary to allow better readability of the tweet. This new task has attracted a great deal of attention recently. Given a tweet, the aim of tweet contextualization is to produce an informative paragraph, called a context, from a set of documents in response to topics treated by the tweet. Furthermore of being informative, a summary should be coherent, i.e., well-written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach of tweet contextualization based on graphs by combining sentence extraction, sentence aggregation and sentence reordering to enhance informativeness and readability in order to build a relevant and coherent context. The main idea of our proposed method is to select relevant, informative coherent and semantically related sentences from a document that best describes themes expressed by the tweet, and aggregate relevant phrases in the same graph to filter more informative ones. We proposed a novel algorithm called CSA algorithm to achieve our aim and to construct a concise extract. We also proposed to invest in a reordering phase to improve the coherence of the obtained context.

New Information Retrieval Approach Based on Semantic Indexing by Meaning

Proceedings of the 16th International Conference on Applied Computing 2019, Nov 7, 2019

An Information Retrieval System (IRS) offers a number of tools and techniques, which enable to lo... more An Information Retrieval System (IRS) offers a number of tools and techniques, which enable to locate and visualize the relevant information needed. This information, is expressed by the user in the form of a query natural language. However, the representation of documents and the query in a traditional IRS lead to a lexical-centered relevance estimation which is, in fact, less efficient than a semantic-focused estimation. As a consequence, the documents that are actually relevant are not being recovered if they do not share words with the query, while the documents non relevant, which are words in common with the query, are recovered even though at times they do not have the meaning intended. This paper tackles this problem while suggesting a solution in the level of indexation of an IRS allowing it to improve its performance. To be more precise, we suggest a new approach of semantic indexation allowing to lead to the exact meaning of each term in a document or query undergoing a contextual analysis at the sentence level. In fact, if the system is able to comprehend the need of the user, then consequently it is perfectly capable to respond to it. Add to that, we suggest a simple method allowing to apply any model of IR on our new index table without changing its original bases making it faster. In order to validate this proposed approach, this new created system is evaluated base on numerous collections naming "TIME", "BBC", "The Guardian" and "BigThink". The results based on the experiments indicate the efficacy of our hypothesis compared to traditional IR approaches.

Download

Possibilistic Information Retrieval Model Based on Relevant Annotations and Expanded Classification

Lecture Notes in Computer Science, 2015

The heterogeneity and the great mass of information found on the web today require an information... more The heterogeneity and the great mass of information found on the web today require an information treatment before being used. The annotations, like all other information, must be filtered to determine those that are relevant. The new concept of “relevant annotation” can be then, considered as a new source of evidence. In addition to the vast amount of annotations, we notice that annotations express generally brief ideas using some words that they cannot be comprehensible independently of his context. This is why, we thought to classify it in clusters annotations sharing the same context and semantically related. In this paper, we propose a new model based on clustering for the classification and probabilistic model for the filtering. In the experiments, we tried to consider the relevant annotation classes as a new source of information able to improve the collaborative information retrieval.

G-Form: A Collaborative Design Approach to Regard Deep Web Form as Galaxy of Concepts

Lecture Notes in Computer Science, 2015

Deep web is growing rapidly with multitude of devices and rendering capability. Despite the richn... more Deep web is growing rapidly with multitude of devices and rendering capability. Despite the richness of deep Web forms, their rendering methodology is very poor in terms of capacity of expression. Hence, user has no indication about the richness of query and query capability when he interprets this interface. In this paper we propose a new rendering approach of deep Web forms which is easy to interpret by user and reflects the exact meaning of query. We have evaluated our algorithm on standard dataset and compared it to a well known state of the art algorithm. Our approach has proved good performances with respect to standard measures.

Relevance Feedback for Collaborative Retrieval Based on Semantic Annotations

A collaborative retrieval, based on the concept of sharing between users, is increasingly used to... more A collaborative retrieval, based on the concept of sharing between users, is increasingly used to facilitate the research and to satisfy the needs. In this context, we suggest to improve the performance of collaborative research, taking account of the annotations as a new source of information describing the documents. In our contribution, we suggest to apply the relevance feedback to expand the user query. We also suggest a new approach based on co-occurrence to extract the relevant terms from annotations in the semi-structured documents returned by the collaborative retrieval systems. Experiments have shown their interest especially for the top ten documents returned by the system that have exceeded a value of 70% as the improvement rate.

Download

VIQI: A new approach for visual interpretation of deep web query interfaces

2012 International Conference on Information Technology and e-Services, 2012

Deep Web databases contain more than 90% of pertinent information of the Web. Despite their impor... more Deep Web databases contain more than 90% of pertinent information of the Web. Despite their importance, users don't profit of this treasury. Many deep web services are offering competitive services in term of prices, quality of service, and facilities. As the number of services is growing rapidly, users have difficulty to ask many web services in the same time. In this paper, we imagine a system where users have the possibility to formulate one query using one query interface and then the system translates query to the rest of query interfaces. However, interfaces are created by designers in order to be interpreted visually by users, machines cannot interpret query from a given interface. We propose a new approach which emulates capacity of interpretation of users and extracts query from deep web query interfaces. Our approach has proved good performances on two standard datasets.

Download

Information Retrieval from Deep Web Based on Visual Query Interpretation

International Journal of Information Retrieval Research, 2012

Deep Web is growing rapidly. More than 90% of relevant information in web comes from deep Web. Us... more Deep Web is growing rapidly. More than 90% of relevant information in web comes from deep Web. Users are usually interested by products which satisfy their needs at the best prices and quality of service .Hence, user’s needs concerns not only one service but many competitive services at the same time. However, for commercial reasons, there is no way to compare all web services products. Each web service is a black box which accepts queries through its own query interface and returns results. As consequence, users ask separately different web services and spend a lot of time comparing products in order to find the best one. This is a burden for novice users. In this paper, the authors propose a new approach which integrates query interfaces of many web services into one universal web service. The new interface describes visually the universal query and is used to ask many web services at the same time. The authors have evaluated their approach on standard datasets and have proved goo...

Download

G-Form: A New Approach for Visual Interpretation of Deep Web Form as Galaxy of Concepts

Download

CORIA 2009- Conférence en Recherche d'Information et Applications Survey of the Adequate Descriptor for Content-Based Image Retrieval on the Web: Global versus Local Features

ABSTRACT. The need for efficient content-based image retrieval has increased hugely. Two methods ... more ABSTRACT. The need for efficient content-based image retrieval has increased hugely. Two methods are recognized for describing the content of images: using global features and using local features. In this paper, we propose two methods for image retrieving based on visual similarity. The first one characterizes images by global features, when the second is based on local features. In the global descriptor attributes are computed on the whole image, whereas in the local descriptor attributes are computed on regions of the image. The aim of this paper is to compare global features versus local features for Web images retrieval. RÉSUMÉ. On reconnait actuellement, dans les systèmes de recherche d’image par contenu, deux méthodes pour la description du contenu des images: à travers des attributs locaux ou à travers des attributs globaux. Dans ce papier, nous proposons deux méthodes pour la recherche d’image qui sont basées sur la similitude visuelle. La première caractérise les images pa...

Download

lobna hlaoua

Uploads

Papers by lobna hlaoua

Log In