Knowledge Discovery in Documents by Extracting Frequent Word Sequences

published or submitted for publicatio

XML Schema Clustering with Semantic and Hierarchical Similarity Measures

With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis

Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam

KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.Comment: 9 page

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 contarían 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 podrían 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 científicos del área de Lingüística 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

Maximal frequent sequences applied to drug-drug interaction extraction

A drug-drug interaction (DDI) occurs when the effects of a drug are modified by the presence of other drugs. DDIs can decrease therapeutic benefit or efficacy of treatments and this could have very harmful consequences in the patient's health that could even cause the patient's death. Knowing the interactions between prescribed drugs is of great clinical importance, it is very important to keep databases up-to-date with respect to new DDI. In this thesis we aim to build a system to assist healthcare professionals to be updated about published drug-drug interactions. The goal of this thesis is to study a method based on maximal frequent sequences (MFS) and machine learning techniques in order to automatically detect interactions between drugs in pharmacological and medical literature. With the study of these methods, the IT community will assist healthcare community to update their drug interactions database in a fast and semi-automatic way. In a first solution, we classify pharmacological sentences depending on whether or not they are describing a drug-drug interaction. This would enable to automatically find sentences containing drug-drug interactions. This solution is completely based in maximal frequent sequences (MFS) extracted from a set of test documents. In a second solution based in machine learning, we go further in the search and perform DDI extraction, determining if two specific drugs appearing in a sentence interact or not. This can be used as an assisting tool to populate databases with drug-drug interactions. The machine learning classifier is trained with several features i.e., bag of words, word categories, MFS, token and char level features and drug level features. The classifier we used was a Random Forest. This system was sent to the DDIExtraction 2011 competition and reached the 6th position. Finally, we introduce Maximal Frequent Discriminative Sequences (MFDS), a novel method of sequential pattern discovery that extends the concept of MFS to adapt it to classification tasks.García Blasco, S. (2012). Maximal frequent sequences applied to drug-drug interaction extraction. http://hdl.handle.net/10251/15342Archivo delegad

Collective emotions online and their influence on community life

E-communities, social groups interacting online, have recently become an object of interdisciplinary research. As with face-to-face meetings, Internet exchanges may not only include factual information but also emotional information - how participants feel about the subject discussed or other group members. Emotions are known to be important in affecting interaction partners in offline communication in many ways. Could emotions in Internet exchanges affect others and systematically influence quantitative and qualitative aspects of the trajectory of e-communities? The development of automatic sentiment analysis has made large scale emotion detection and analysis possible using text messages collected from the web. It is not clear if emotions in e-communities primarily derive from individual group members' personalities or if they result from intra-group interactions, and whether they influence group activities. We show the collective character of affective phenomena on a large scale as observed in 4 million posts downloaded from Blogs, Digg and BBC forums. To test whether the emotions of a community member may influence the emotions of others, posts were grouped into clusters of messages with similar emotional valences. The frequency of long clusters was much higher than it would be if emotions occurred at random. Distributions for cluster lengths can be explained by preferential processes because conditional probabilities for consecutive messages grow as a power law with cluster length. For BBC forum threads, average discussion lengths were higher for larger values of absolute average emotional valence in the first ten comments and the average amount of emotion in messages fell during discussions. Our results prove that collective emotional states can be created and modulated via Internet communication and that emotional expressiveness is the fuel that sustains some e-communities.Comment: 23 pages including Supporting Information, accepted to PLoS ON