1,718 research outputs found
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm usersâ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to âunannotatedâ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the âBag of Visual Wordsâ
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ânon-informative
visual wordsâ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
Dynamic Document Annotation for Efficient Data Retrieval
Document annotation is considered as one of the most popular methods, where metadata present in document is used to search documents from a large text documents database. Few application domains such as scientific networks, blogs share information in a large amount is usually in unstructured data text documents. Manual annotation of each document becomes a tedious task. Annotations facilitate the task of finding the document topic and assist the reader to quickly overview and understand document. Dynamic document annotation provides a solution to such type of problems. Dynamic annotation of documents is generally considered as a semi-supervised learning task. The documents are dynamically assigned to one of a set of predefined classes based on the features extracted from their textual content. This paper proposes survey on Collaborative Adaptive Data sharing platform (CADS) for document annotation and use of query workload to direct the annotation process. A key novelty of CADS is that it learns with time the most important data attributes of the application, and uses this knowledge to guide the data insertion and querying
Mining XML Documents
XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the increasing size of XML collections as information sources, mining techniques that traditionally exist for text collections or databases need to be adapted and new methods to be invented to exploit the particular structure of XML documents. Basically XML documents can be seen as trees, which are well known to be complex structures. This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. We focus on three mining tasks: classification and clustering which are standard for text collections; discovering of frequent tree structure which is especially important for heterogeneous collection. This chapter presents some recent approaches and algorithms to support these tasks together with experimental evaluation on a variety of large XML collections
Automated retrieval and extraction of training course information from unstructured web pages
Web Information Extraction (WIE) is the discipline dealing with the discovery, processing and extraction of specific pieces of information from semi-structured or unstructured web pages. The World Wide Web comprises billions of web pages and there is much need for systems that will locate, extract and integrate the acquired knowledge into organisations practices. There are some commercial, automated web extraction software packages, however their success comes from heavily involving their users in the process of finding the relevant web pages, preparing the system to recognise items of interest on these pages and manually dealing with the evaluation and storage of the extracted results.
This research has explored WIE, specifically with regard to the automation of the extraction and validation of online training information. The work also includes research and development in the area of automated Web Information Retrieval (WIR), more specifically in Web Searching (or Crawling) and Web Classification. Different technologies were considered, however after much consideration, NaĂŻve Bayes Networks were chosen as the most suitable for the development of the classification system. The extraction part of the system used Genetic Programming (GP) for the generation of web extraction solutions. Specifically, GP was used to evolve Regular Expressions, which were then used to extract specific training course information from the web such as: course names, prices, dates and locations.
The experimental results indicate that all three aspects of this research perform very well, with the Web Crawler outperforming existing crawling systems, the Web Classifier performing with an accuracy of over 95% and a precision of over 98%, and the Web Extractor achieving an accuracy of over 94% for the extraction of course titles and an accuracy of just under 67% for the extraction of other course attributes such as dates, prices and locations. Furthermore, the overall work is of great significance to the sponsoring company, as it simplifies and improves the existing time-consuming, labour-intensive and error-prone manual techniques, as will be discussed in this thesis. The prototype developed in this research works in the background and requires very little, often no, human assistance
Enhancing Productivity of Recruitment Process Using Data Mining & Text Mining Tools
Digital communication has significantly reduced the time it takes to send a rĂ©sumĂ©, but the recruiterâs work has become more complicated because with this technological advancement they get more rĂ©sumĂ©s for each job opening. It becomes almost impossible to physically scan each rĂ©sumĂ© that meets their organizationâs job requirement. The filtering and search techniques provide hundreds of rĂ©sumĂ©s that can fulfill the desired criteria. Most approaches focus on either parsing the rĂ©sumĂ© to get information or propose some filtering methods. Moreover, rĂ©sumĂ©s vary in format and style, making it difficult to maintain a structural repository which would contain all the necessary information. The goal of this project is to examine and propose an approach which would consider the skill sets from the potential rĂ©sumĂ©s, along with expertise domains like related work experience and education, to score the selected ârelevant rĂ©sumĂ©.â This approach aims at highlighting the most important and relevant rĂ©sumĂ©s, thus saving an enormous amount of time and effort that is required fo
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
Editorial for the First Workshop on Mining Scientific Papers: Computational Linguistics and Bibliometrics
The workshop "Mining Scientific Papers: Computational Linguistics and
Bibliometrics" (CLBib 2015), co-located with the 15th International Society of
Scientometrics and Informetrics Conference (ISSI 2015), brought together
researchers in Bibliometrics and Computational Linguistics in order to study
the ways Bibliometrics can benefit from large-scale text analytics and sense
mining of scientific papers, thus exploring the interdisciplinarity of
Bibliometrics and Natural Language Processing (NLP). The goals of the workshop
were to answer questions like: How can we enhance author network analysis and
Bibliometrics using data obtained by text analytics? What insights can NLP
provide on the structure of scientific writing, on citation networks, and on
in-text citation analysis? This workshop is the first step to foster the
reflection on the interdisciplinarity and the benefits that the two disciplines
Bibliometrics and Natural Language Processing can drive from it.Comment: 4 pages, Workshop on Mining Scientific Papers: Computational
Linguistics and Bibliometrics at ISSI 201
Bridging the gap within text-data analytics: A computer environment for data analysis in linguistic research
[EN] Since computer technology became widespread available at universities during the last quarter of the twentieth century, language researchers have been successfully employing software to analyse usage patterns in corpora. However, although there has been a proliferation of software for different disciplines within text-data analytics, e.g. corpus linguistics, statistics, natural language processing and text mining, this article demonstrates that any computer environment intended to support advanced linguistic research more effectively should be grounded on a user-centred approach to holistically integrate cross-disciplinary methods and techniques in a linguist-friendly manner. To this end, I examine not only the tasks that are derived from linguists' needs and goals but also the technologies that appropriately deal with the properties of linguistic data. This research results in the implementation of DAMIEN, an online workbench designed to conduct linguistic experiments on corpora.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2014-53788-C3-1-P.Periñån Pascual, C. (2017). Bridging the gap within text-data analytics: A computer environment for data analysis in linguistic research. LFE. Revista de Lenguas para Fines EspecĂficos. 23(2):111-132. https://doi.org/10.20420/rlfe.2017.175S11113223
Bridging the gap within text-data analytics: a computer environment for data analysis in linguistic research
Since computer technology became widespread available at universities during the last quarter of the twentieth century, language researchers have been successfully employing software to analyse usage patterns in corpora. However, although there has been a proliferation of software for different disciplines within text-data analytics, e.g. corpus linguistics, statistics, natural language processing and text mining, this article demonstrates that any computer environment intended to support advanced linguistic research more effectively should be grounded on a user-centred approach to holistically integrate cross-disciplinary methods and techniques in a linguist-friendly manner. To this end, I examine not only the tasks that are derived from linguists' needs and goals but also the technologies that appropriately deal with the properties of linguistic data. This research results in the implementation of DAMIEN, an online workbench designed to conduct linguistic experiments on corpora
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