Carlos Cobos

Extractive single-document summarization based on genetic operators and guided local search

Due to the exponential growth of textual information available on the Web, end users need to be able to access information in summary form - and without losing the most important information in the document when generating the summaries. Automatic generation of extractive summaries from a single document has traditionally been given the task of extracting the most relevant sentences from the original document. The methods employed generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. These features include the position of the sentence in the document, its similarity to the title, the sentence length, and the frequency of the terms in the sentence. However, it has still not been possible to achieve a quality of summary that matches that performed by humans and therefore methods continue to be brought forward that aim to improve on the results. This paper addresses the generation of extractive summaries from a single document as a binary optimization problem where the quality (fitness) of the solutions is based on the weighting of individual statistical features of each sentence - such as position, sentence length and the relationship of the summary to the title, combined with group features of similarity between candidate sentences in the summary and the original document, and among the candidate sentences of the summary. This paper proposes a method of extractive single-document summarization based on genetic operators and guided local search, called MA-SingleDocSum. A memetic algorithm is used to integrate the own-population-based search of evolutionary algorithms with a guided local search strategy. The proposed method was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking, using ROUGE measures on the datasets DUC2001 and DUC2002. The results showed that MA-SingleDocSum outperforms the state of the art methods.

A hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes

To carry out effective teaching/learning processes, lecturers in a variety of educational institutions frequently need support. They therefore resort to advice from more experienced lecturers, to formal training processes such as specializations, master or doctoral degrees, or to self-training. High costs in time and money are invariably involved in the processes of formal training, while self-training and advice each bring their own specific risks (e.g. of following new trends that are not fully evaluated or the risk of applying techniques that are inappropriate in specific contexts).This paper presents a system that allows lecturers to define their best teaching strategies for use in the context of a specific class. The context is defined by: the specific characteristics of the subject being treated, the specific objectives that are expected to be achieved in the classroom session, the profile of the students on the course, the dominant characteristics of the teacher, and the classroom environment for each session, among others. The system presented is the Recommendation System of Pedagogical Patterns (RSPP). To construct the RSPP, an ontology representing the pedagogical patterns and their interaction with the fundamentals of the educational process was defined. A web information system was also defined to record information on courses, students, lecturers, etc.; an option based on a unified hybrid model (for content and collaborative filtering) of recommendations for pedagogical patterns was further added to the system. RSPP features a minable view, a tabular structure that summarizes and organizes the information registered in the rest of the system as well as facilitating the task of recommendation. The data recorded in the minable view is taken to a latent space, where noise is reduced and the essence of the information contained in the structure is distilled. This process makes use of Singular Value Decomposition (SVD), commonly used by information retrieval and recommendation systems. Satisfactory results both in the accuracy of the recommendations and in the use of the general application open the door for further research and expand the role of recommender systems in educational teacher support processes.

Web document clustering based on Global-Best Harmony Search, K-means, Frequent Term Sets and Bayesian Information Criterion

This paper introduces a new description-centric algorithm for web document clustering based on the hybridization of the Global-Best Harmony Search with the K-means algorithm, Frequent Term Sets and Bayesian Information Criterion. The new algorithm defines the number of clusters automatically. The Global-Best Harmony Search provides a global strategy for a search in the solution space, based on the Harmony Search and the concept of swarm intelligence. The K-means algorithm is used to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called IGBHSK, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then evaluated by a group of users.

Web document clustering based on a new niching Memetic Algorithm, Term-Document Matrix and Bayesian Information Criterion

This paper introduces a new description-centric algorithm for web document clustering based on Memetic Algorithms with Niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines the number of clusters automatically. The Memetic Algorithm provides a combined global and local strategy for a search in the solution space and the Niching methods to promote diversity in the population and prevent the population from converging too quickly (based on restricted competition replacement and restrictive mating). The Memetic Algorithm uses the K-means algorithm to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called WDC-NMA, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then initially evaluated by a group of users.

A hyper-heuristic approach to design and tuning heuristic methods for web document clustering

This paper introduces a new description-centric algorithm for web document clustering called HHWDC. The HHWDC algorithm has been designed from a hyper-heuristic approach and allows defining the best algorithm for web document clustering. HHWDC uses as heuristic selection methodology two options, namely: random selection and roulette wheel selection based on performance of low-level heuristics (harmony search, an improved harmony search, a novel global harmony search, global-best harmony search, restrictive mating, roulette wheel selection, and particle swarm optimization). HHWDC uses the k-means algorithm for local solution improvement strategy, and based on the Bayesian Information Criteria is able to automatically define the number of clusters. HHWDC uses two acceptance/replace strategies, namely: Replace the worst and Restricted Competition Replacement. HHWDC was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm).

Grouping of business processes models based on an incremental clustering algorithm using fuzzy similarity and multimodal search

A model for searching business processes, based on a multimodal approach that integrates textual and structural information.A clustering mechanism that uses a similarity function based on fuzzy logic for grouping search results.Evaluation of search method using internal quality assessment and external assessment based on human criteria. Nowadays, many companies standardize their operations through Business Process (BP), which are stored in repositories and reused when new functionalities are required. However, finding specific processes may become a cumbersome task due to the large size of these repositories. This paper presents MulTimodalGroup, a model for grouping and searching business processes. The grouping mechanism is built upon a clustering algorithm that uses a similarity function based on fuzzy logic; this grouping is performed using the results of each user request. By its part, the search is based on a multimodal representation that integrates textual and structural information of BP. The assessment of the proposed model was carried out in two phases: 1) internal quality assessment of groups and 2) external assessment of the created groups compared with an ideal set of groups. The assessment was performed using a closed BP collection designed collaboratively by 59 experts. The experimental results in each phase are promising and evidence the validity of the proposed model.

Multidimensional analysis model for a document warehouse that includes textual measures

Data warehouses and On-Line Analytical Processing tools, OLAP, together permit a multi-dimensional analysis of structured data information. However, as business systems are increasingly required to handle substantial quantities of unstructured textual information, the need arises for an effective and similar means of analysis. To manage unstructured text data stored in data warehouses, a new multi-dimensional analysis model is proposed that includes textual measures as well as a topic hierarchy. In this model, the textual measures that associate the topics with the text documents are generated by Probabilistic Latent Semantic Analysis, while the hierarchy is created automatically using a clustering algorithm. Documents are then able to be queried using OLAP tools. The model was evaluated from two viewpoints - query execution time and user satisfaction. Evaluation of execution time was carried out on scientific articles using two query types and user satisfaction (with query time and ease of use) using statistical frequency and multivariate analyses. Encouraging observations included that as the number of documents increases, query time increases as a lineal, rather than exponential tendency. In addition, the model gained an increasing acceptance with use, while the visualization of the model was also well received by users. A new multidimensional model that integrates text based on three textual measures.The granularity of proposed model is at document level.The model allows getting topics according to dimensions implied in the query.The model allows getting documents according to dimensions implied in the query.The model allows getting the words or terms for each topic.

Business Processes Retrieval based on Multimodal Search and Lingo Clustering Algorithm

This paper presents a model for searching and grouping of business process models. To search business process models, the model contains a module for multimodal indexing that takes into account textual and structural information of models. To group models, an adaptation of the Lingo algorithm was used; it is based on singular value decomposition and frequent phrases extracted from the description (textual and structural information) of the business process models retrieved according to a query. The evaluation of model was conducted by executing the search process on a closed test-collection built collaboratively, that containing 146 business process models, and comparing the results with a set of relevant business process models obtained from an evaluation issued by 59 experts. Measures of relevance such as graded precision and recall shown promising results in the search process, as well as the quality assessment of the sets formed by the grouping process.

Dynamic Reconfiguration of Composite Convergent Services Supported by Multimodal Search

Composite convergent services integrate a set of functionalities from Web and Telecommunication domains. Due to the big amount of available functionalities, automation of composition process is required in many fields. However, automated composition is not feasible in practice if reconfiguration mechanisms are not considered. This paper presents a novel approach for dynamic reconfiguration of convergent services that replaces malfunctioning regions of composite convergent services considering user preferences. In order to replace the regions of services, a multimodal search is performed. Our contributions are: a model for representing composite convergent services and a region-based algorithm for reconfiguring services supported by multimodal search.

TopicSearch - Personalized Web Clustering Engine Using Semantic Query Expansion, Memetic Algorithms and Intelligent Agents

As resources become more and more available on the Web, so the difficulties associated with finding the desired information increase. Intelligent agents can assist users in this task since they can search, filter and organize information on behalf of their users. Web document clustering techniques can also help users to find pages that meet their information requirements. This paper presents a personalized web document clustering called TopicSearch. TopicSearch introduces a novel inverse document frequency function to improve the query expansion process, a new memetic algorithm for web document clustering, and frequent phrases approach for defining cluster labels. Each user query is handled by an agent who coordinates several tasks including query expansion, search results acquisition, preprocessing of search results, cluster construction and labeling, and visualization. These tasks are performed by specialized agents whose execution can be parallelized in certain instances. The model was successfully tested on fifty DMOZ datasets. The results demonstrated improved precision and recall over traditional algorithms (k-means, Bisecting k-means, STC y Lingo). In addition, the presented model was evaluated by a group of twenty users with 90% being in favor of the model.