Extracting interactions between proteins from the literature

During the last decade, biomedicine has witnessed a tremendous development. Large amounts of experimental and computational biomedical data have been generated along with new discoveries, which are accompanied by an exponential increase in the number of biomedical publications describing these discoveries. In the meantime, there has been a great interest with scientific communities in text mining tools to find knowledge such as protein-protein interactions, which is most relevant and useful for specific analysis tasks. This paper provides a outline of the various information extraction methods in biomedical domain, especially for discovery of protein-protein interactions. It surveys methodologies involved in plain texts analyzing and processing, categorizes current work in biomedical information extraction, and provides examples of these methods. Challenges in the field are also presented and possible solutions are discussed.

From biomedical literature to knowledge:mining protein-protein interactions

To date, more than 16 million citations of published articles in biomedical domain are available in the MEDLINE database. These articles describe the new discoveries which accompany a tremendous development in biomedicine during the last decade. It is crucial for biomedical researchers to retrieve and mine some specific knowledge from the huge quantity of published articles with high efficiency. Researchers have been engaged in the development of text mining tools to find knowledge such as protein-protein interactions, which are most relevant and useful for specific analysis tasks. This chapter provides a road map to the various information extraction methods in biomedical domain, such as protein name recognition and discovery of protein-protein interactions. Disciplines involved in analyzing and processing unstructured-text are summarized. Current work in biomedical information extracting is categorized. Challenges in the field are also presented and possible solutions are discussed.

Using the Dempster-Shafer theory of evidence to resolve ABox inconsistencies

Automated ontology population using information extraction algorithms can produce inconsistent knowledge bases. Confidence values assigned by the extraction algorithms may serve as evidence in helping to repair inconsistencies. The Dempster-Shafer theory of evidence is a formalism, which allows appropriate interpretation of extractors’ confidence values. This chapter presents an algorithm for translating the subontologies containing conflicts into belief propagation networks and repairing conflicts based on the Dempster-Shafer plausibility.

Words and intelligence II:essays in honor of Yorick Wilks

Yorick Wilks is a central figure in the fields of Natural Language Processing and Artificial Intelligence. His influence has extends to many areas of these fields and includes contributions to Machine Translation, word sense disambiguation, dialogue modeling and Information Extraction.This book celebrates the work of Yorick Wilks from the perspective of his peers. It consists of original chapters each of which analyses an aspect of his work and links it to current thinking in that area. His work has spanned over four decades but is shown to be pertinent to recent developments in language processing such as the Semantic Web.This volume forms a two-part set together with Words and Intelligence I, Selected Works by Yorick Wilks, by the same editors.

Almost Separable Data Aggregation by Layers of Formal Neurons

Information extraction or knowledge discovery from large data sets should be linked to data aggregation process. Data aggregation process can result in a new data representation with decreased number of objects of a given set. A deterministic approach to separable data aggregation means a lesser number of objects without mixing of objects from different categories. A statistical approach is less restrictive and allows for almost separable data aggregation with a low level of mixing of objects from different categories. Layers of formal neurons can be designed for the purpose of data aggregation both in the case of deterministic and statistical approach. The proposed designing method is based on minimization of the of the convex and piecewise linear (CPL) criterion functions.

Automatic Discovery of Word Semantic Relations

In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. This proposal di®ers from previous research as it tries to take the best of two different methodologies i.e. semantic space models and information extraction models. It can be applied to extract close semantic relations, it limits the search space and it is unsupervised.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Towards next generation vertical search engines

As the Web evolves unexpectedly fast, information grows explosively. Useful resources become more and more difficult to find because of their dynamic and unstructured characteristics. A vertical search engine is designed and implemented towards a specific domain. Instead of processing the giant volume of miscellaneous information distributed in the Web, a vertical search engine targets at identifying relevant information in specific domains or topics and eventually provides users with up-to-date information, highly focused insights and actionable knowledge representation. As the mobile device gets more popular, the nature of the search is changing. So, acquiring information on a mobile device poses unique requirements on traditional search engines, which will potentially change every feature they used to have. To summarize, users are strongly expecting search engines that can satisfy their individual information needs, adapt their current situation, and present highly personalized search results. ^ In my research, the next generation vertical search engine means to utilize and enrich existing domain information to close the loop of vertical search engine's system that mutually facilitate knowledge discovering, actionable information extraction, and user interests modeling and recommendation. I investigate three problems in which domain taxonomy plays an important role, including taxonomy generation using a vertical search engine, actionable information extraction based on domain taxonomy, and the use of ensemble taxonomy to catch user's interests. As the fundamental theory, ultra-metric, dendrogram, and hierarchical clustering are intensively discussed. Methods on taxonomy generation using my research on hierarchical clustering are developed. The related vertical search engine techniques are practically used in Disaster Management Domain. Especially, three disaster information management systems are developed and represented as real use cases of my research work.^

Uso de correntropia na generalização de funções cicloestacionárias e aplicações para a extração de características de sinais modulados

Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract second-order cyclostationary information. However, second-order cyclostationary information is poor in nongaussian signals, as the cyclostationary analysis in this case should comprise higher-order statistical information. This paper proposes a new mathematical tool for the higher-order cyclostationary analysis based on the correntropy function. Specifically, the cyclostationary analysis is revisited focusing on the information theory, while the Cyclic Correntropy Function (CCF) and Cyclic Correntropy Spectral Density (CCSD) are also defined. Besides, it is analytically proven that the CCF contains information regarding second- and higher-order cyclostationary moments, being a generalization of the CAF. The performance of the aforementioned new functions in the extraction of higher-order cyclostationary characteristics is analyzed in a wireless communication system where nongaussian noise exists.

Towards Next Generation Vertical Search Engines

As the Web evolves unexpectedly fast, information grows explosively. Useful resources become more and more difficult to find because of their dynamic and unstructured characteristics. A vertical search engine is designed and implemented towards a specific domain. Instead of processing the giant volume of miscellaneous information distributed in the Web, a vertical search engine targets at identifying relevant information in specific domains or topics and eventually provides users with up-to-date information, highly focused insights and actionable knowledge representation. As the mobile device gets more popular, the nature of the search is changing. So, acquiring information on a mobile device poses unique requirements on traditional search engines, which will potentially change every feature they used to have. To summarize, users are strongly expecting search engines that can satisfy their individual information needs, adapt their current situation, and present highly personalized search results. In my research, the next generation vertical search engine means to utilize and enrich existing domain information to close the loop of vertical search engine's system that mutually facilitate knowledge discovering, actionable information extraction, and user interests modeling and recommendation. I investigate three problems in which domain taxonomy plays an important role, including taxonomy generation using a vertical search engine, actionable information extraction based on domain taxonomy, and the use of ensemble taxonomy to catch user's interests. As the fundamental theory, ultra-metric, dendrogram, and hierarchical clustering are intensively discussed. Methods on taxonomy generation using my research on hierarchical clustering are developed. The related vertical search engine techniques are practically used in Disaster Management Domain. Especially, three disaster information management systems are developed and represented as real use cases of my research work.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

Koherentziazko diskurtso erlazioen detekzio automatikoa patroien bidez, XMLko erlazio-egiturak oinarri hartuta

[EU]Hizkuntzaren prozesamenduan testu koherenteetan kausa taldeko erlazioak (KAUSA, ONDORIOA eta HELBURUA) automatikoki hautematea eta bereiztea erabilgarria da galdera-erantzun automatikoko sistemak eraikitzerako orduan. Horretarako Egitura Erretorikoaren Teoria (Rhetorical Structure Theory, aurrerantzean RST) eta bere erlazioak erabiliko ditugu, corpus bezala RST Treebank -a (Iruskieta et al., 2013) hartuta, zientziako laburpen-testuz osatutako corpusa, hain zuzen ere. Corpus hori XML formatuan deskargatu eta hortik XPATH tresnaren bidez informazio garrantzitsuena eskuratzen dugu. Lan honek 3 helburu nagusi ditu: lehendabizi, kausa taldeko erlazioak elkarren artean bereiztea, bigarrenez, kausa taldeko erlazio hauek beste erlazio guztiekin bereiztea, eta azkenik, EBALUAZIOA eta INTERPRETAZIOA erlazioak bereiztea sentimendu analisian aplikatu ahal izateko. Ataza horiek egiteko, RhetDB tresnarekin eskuratu diren patroi ensaguratsuenak erabili eta bi aplikazio garatu ditugu. Alde batetik, bilatu nahi ditugun patroiak adierazi eta erlazio-egitura duen edonolako testuetan bilaketak egiten dituen bilatzailea, eta bestetik, patroi esanguratsuenak emanda erlazioak etiketatzen dituen etiketatzailea. Bi aplikazio hauek gainera, ahalik eta modu parametrizagarrienean erabiltzeko garatu ditugu, kodea aldatu gabe edonork erabili ahal izateko antzeko atazak egiteko. Etiketatzaileak ebaluatu ondoren, identifikatzeko erlaziorik errazena HELBURUA erlazioa dela ikusi dugu eta KAUSA eta ONDORIOA bereizteko arazo gehiago dauzkagula ere ondorioztatu dugu. Modu berean, EBALUAZIOA eta INTERPRETAZIOA ere elkarren artean bereiz dezakegula ikusi dugu.

Image summarisation: human action description from static images

Dissertação de Mestrado, Processamento de Linguagem Natural e Indústrias da Língua, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2014

Criação de uma ontologia e respectiva povoação a partir do processamento de relatórios médicos

A evolução tecnológica tem provocado uma evolução na medicina, através de sistemas computacionais voltados para o armazenamento, captura e disponibilização de informações médicas. Os relatórios médicos são, na maior parte das vezes, guardados num texto livre não estruturado e escritos com vocabulário proprietário, podendo ocasionar falhas de interpretação. Através das linguagens da Web Semântica, é possível utilizar antologias como modo de estruturar e padronizar a informação dos relatórios médicos, adicionando¬ lhe anotações semânticas. A informação contida nos relatórios pode desta forma ser publicada na Web, permitindo às máquinas o processamento automático da informação. No entanto, o processo de criação de antologias é bastante complexo, pois existe o problema de criar uma ontologia que não cubra todo o domínio pretendido. Este trabalho incide na criação de uma ontologia e respectiva povoação, através de técnicas de PLN e Aprendizagem Automática que permitem extrair a informação dos relatórios médicos. Foi desenvolvida uma aplicação, que permite ao utilizador converter relatórios do formato digital para o formato OWL. ABSTRACT: Technological evolution has caused a medicine evolution through computer systems which allow storage, gathering and availability of medical information. Medical reports are, most of the times, stored in a non-structured free text and written in a personal way so that misunderstandings may occur. Through Semantic Web languages, it’s possible to use ontology as a way to structure and standardize medical reports information by adding semantic notes. The information in those reports can, by these means, be displayed on the web, allowing machines automatic information processing. However, the process of creating ontology is very complex, as there is a risk creating of an ontology that not covering the whole desired domain. This work is about creation of an ontology and its population through NLP and Machine Learning techniques to extract information from medical reports. An application was developed which allows the user to convert reports from digital for¬ mat to OWL format.

Semi-automatic keyword based approach for FIRE 2016 Microblog Track

This paper describes our semi-automatic keyword based approach for the four topics of Information Extraction from Microblogs Posted during Disasters task at Forum for Information Retrieval Evaluation (FIRE) 2016. The approach consists three phases.