1,490 research outputs found
Data mining in soft computing framework: a survey
The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included
Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources
Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen
Recent Trends in Computational Intelligence
Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications
Feature Extraction and Duplicate Detection for Text Mining: A Survey
Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Proce- ssing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algo- rithms are needed to extract useful features from huge amount of data. The survey covers different text summarization, classi- fication, clustering methods to discover useful features and also discovering query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query thereby reducing time taken by the user. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. Hence we also review the literature on duplicate detection and data fusion (remove and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user
Machine Learning-Driven Decision Making based on Financial Time Series
L'abstract è presente nell'allegato / the abstract is in the attachmen
Automatic text summarization with Maximal Frequent Sequences
En las últimas dos décadas un aumento exponencial de la información electrónica
ha provocado una gran necesidad de entender rápidamente grandes
volúmenes de información. En este libro se desarrollan los métodos automáticos
para producir un resumen. Un resumen es un texto corto que transmite la información
más importante de un documento o de una colección de documentos. Los
resúmenes utilizados en este libro son extractivos: una selección de las oraciones
más importantes del texto. Otros retos consisten en generar resúmenes de manera
independiente de lenguaje y dominio.
Se describe la identificación de cuatro etapas para generación de resúmenes
extractivos. La primera etapa es la selección de términos, en la que uno tiene
que decidir qué unidades contarÃan como términos individuales. El proceso de
estimación de la utilidad de los términos individuales se llama etapa de pesado
de términos. El siguiente paso se denota como pesado de oraciones, donde todas
las secuencias reciben alguna medida numérica de acuerdo con la utilidad de
términos. Finalmente, el proceso de selección de las oraciones más importantes
se llama selección de oraciones. Los diferentes métodos para generación de resúmenes
extractivos pueden ser caracterizados como representan estas etapas.
En este libro se describe la etapa de selección de términos, en la que la detección
de descripciones multipalabra se realiza considerando Secuencias Frecuentes
Maximales (sfms), las cuales adquieren un significado importante, mientras
Secuencias Frecuentes (sf) no maximales, que son partes de otros sf, no deben
de ser consideradas. En la motivación se consideró costo vs. beneficio: existen
muchas sf no maximales, mientras que la probabilidad de adquirir un significado
importante es baja. De todos modos, las sfms representan todas las sfs en el
modo compacto: todas las sfs podrÃan ser obtenidas a partir de todas las sfms
explotando cada sfm al conjunto de todas sus subsecuencias. Se presentan los nuevos métodos basados en grafos, algoritmos de agrupamiento
y algoritmos genéticos, los cuales facilitan la tarea de generación de
resúmenes de textos. Se ha experimentado diferentes combinaciones de las opciones
de selección de términos, pesado de términos, pesado de oraciones y
selección de oraciones para generar los resúmenes extractivos de textos independientes
de lenguaje y dominio para una colección de noticias. Se ha analizado
algunas opciones basadas en descripciones multipalabra considerándolas en los
métodos de grafos, algoritmos de agrupamiento y algoritmos genéticos. Se han
obtenido los resultados superiores al de estado de arte.
Este libro está dirigido a los estudiantes y cientÃficos del área de LingüÃstica
Computacional, y también a quienes quieren saber sobre los recientes avances en
las investigaciones de generación automática de resúmenes de textos.In the last two decades, an exponential increase in the available electronic information
causes a big necessity to quickly understand large volumes of information.
It raises the importance of the development of automatic methods for
detecting the most relevant content of a document in order to produce a shorter
text. Automatic Text Summarization (ats) is an active research area dedicated to
generate abstractive and extractive summaries not only for a single document, but
also for a collection of documents. Other necessity consists in finding method for
ats in a language and domain independent way.
In this book we consider extractive text summarization for single document
task. We have identified that a typical extractive summarization method consists
in four steps. First step is a term selection where one should decide what units
will count as individual terms. The process of estimating the usefulness of the
individual terms is called term weighting step. The next step denotes as sentence
weighting where all the sentences receive some numerical measure according to
the usefulness of its terms. Finally, the process of selecting the most relevant sentences
calls sentence selection. Different extractive summarization methods can
be characterized how they perform these steps.
In this book, in the term selection step, we describe how to detect multiword
descriptions considering Maximal Frequent Sequences (mfss), which bearing important
meaning, while non-maximal frequent sequences (fss), those that are
parts of another fs, should not be considered. Our additional motivation was
cost vs. benefit considerations: there are too many non-maximal fss while their
probability to bear important meaning is lower. In any case, mfss represent all fss
in a compact way: all fss can be obtained from all mfss by bursting each mfs into
a set of all its subsequences.New methods based on graph algorithms, genetic algorithms, and clustering
algorithms which facilitate the text summarization task are presented. We
have tested different combinations of term selection, term weighting, sentence
weighting and sentence selection options for language-and domain-independent
extractive single-document text summarization on a news report collection. We
analyzed several options based on mfss, considering them with graph, genetic,
and clustering algorithms. We obtained results superior to the existing state-ofthe-
art methods.
This book is addressed for students and scientists of the area of Computational
Linguistics, and also who wants to know recent developments in the area of Automatic
Text Generation of Summaries
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