情報検索における意味的ギャップの解消 : トピックモデルを用いた先進的画像探索

Tohoku University徳山豪課

Information fusion for automated question answering

Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. This includes deep parsing, lookups in ontologies, question typing and machine learning of answer patterns appropriate to question forms. In contrast, I have focused on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from different sources. In QA over free text, it is motivated by the frequency with which different answer candidates are found in different locations, leading to a multiplicity of answers. The reason for such multiplicity is, in part, the massive amount of data used for answering, and also its unstructured and heterogeneous content: Besides am¬ biguities in user questions leading to heterogeneity in extractions, systems have to deal with redundancy, granularity and possible contradictory information. Hence the need for answer candidate comparison. While frequency has proved to be a significant char¬ acteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬ define the concept of "answer" within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An "answer model" is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. The graph represents the fusion of information contained in the set of extractions. Different views of the answer model can be produced, capturing the fact that the same answer can be expressed and pre¬ sented in various ways: picture, video, sound, written or spoken language, or a formal data structure. Within this framework, an answer is a structured object contained in the model and retrieved by a strategy to build a particular view depending on the end user (or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬ proves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Intelligent Tourist Routes

A maior parte das pessoas gosta de viajar e o Porto foi eleita a cidade da Europa mais interessante para visitar em 2019. Com grande potencial de atratividade, o Porto conta com infindáveis opções de rotas turísticas. Investigações recentes mostram que um operador eficiente de viagens não só deve ter em conta as necessidades e constrangimentos do utilizador, mas também permitir algum grau de livre exploração da cidade, adaptando a oferta de acordo com as preferências do utilizador. A imagem global do contexto é um bom ponto de partida para uma viagem memorável. Nesta dissertação pretende-se desenvolver sistema inteligente capaz de maximizar a satisfação do visitante, criando percursos dinâmicos e personalizados em função de preferências e interesses dos utilizadores. Estes serão aferidos diretamente através de técnicas modernas de segmentação e descoberta de perfil e indiretamente através da pontuação atribuída pelos utilizadores a sets de fotografias (normais e 360) dos locais de interesse. Ao longo do percurso o utilizador poderá dar feedback sobre os locais de interesse sugeridos por forma a potenciar a aprendizagem do sistema

Mobile Search Engine using Clustering and Query Expansion

Internet content is growing exponentially and searching for useful content is a tedious task that we all deal with today. Mobile phones lack of screen space and limited interaction methods makes traditional search engine interface very inefficient. As the use of mobile internet continues to grow there is a need for an effective search tool. I have created a mobile search engine that uses clustering and query expansion to find relevant web pages efficiently. Clustering organizes web pages into groups that reflect different components of a query topic. Users can ignore clusters that they find irrelevant so they are not forced to sift through a long list of off-topic web pages. Query expansion uses query results, dictionaries, and cluster labels to formulate additional terms to manipulate the original query. The new manipulated query gives a more in depth result that eliminates noise. I believe that these two techniques are effective and can be combined to make the ultimate mobile search engine

Organisation of Web pages Using Document Clustering Techniques: Some Algorithms

With the increase in information on the World Wide Web it has become difficult to find the desired information on search engines. Clustering Techniques are now being used to give a meaningful search result on web. This paper gives an idea about Web Page document clustering, different algorithms including similarity measures and clustering engines

Exploratory Search on Mobile Devices

The goal of this thesis is to provide a general framework (MobEx) for exploratory search especially on mobile devices. The central part is the design, implementation, and evaluation of several core modules for on-demand unsupervised information extraction well suited for exploratory search on mobile devices and creating the MobEx framework. These core processing elements, combined with a multitouch - able user interface specially designed for two families of mobile devices, i.e. smartphones and tablets, have been finally implemented in a research prototype. The initial information request, in form of a query topic description, is issued online by a user to the system. The system then retrieves web snippets by using standard search engines. These snippets are passed through a chain of NLP components which perform an ondemand or ad-hoc interactive Query Disambiguation, Named Entity Recognition, and Relation Extraction task. By on-demand or ad-hoc we mean the components are capable to perform their operations on an unrestricted open domain within special time constraints. The result of the whole process is a topic graph containing the detected associated topics as nodes and the extracted relation ships as labelled edges between the nodes. The Topic Graph is presented to the user in different ways depending on the size of the device she is using. Various evaluations have been conducted that help us to understand the potentials and limitations of the framework and the prototype

Data Driven Approach To Saltwater Disposal (SWD) Well Location Optimization In North Dakota

The sharp increase in oil and gas production in the Williston Basin of North Dakota since 2006 has resulted in a significant increase in produced water volumes. Primary mechanism for disposal of produced water is by injection into underground Inyan Kara formation through Class-II Saltwater Disposal (SWD) wells. With number of SWD wells anticipated to increase from 900 to over 1400 by 2035, localized pressurization and other potential issues that could affect performance of future oil and SWD wells, there was a need for a reliable model to select locations of future SWD wells for optimum performance. Since it is uncommon to develop traditional geological and simulation models for SWD wells, this research focused on developing data-driven proxy models based on the CRISP-Data Mining pipeline for understanding SWD well performance and optimizing future well locations. NDIC’s oil and gas division was identified as the primary data source. Significant efforts went towards identifying other secondary data sources, extracting required data from primary and secondary data sources using web scraping, integrating different data types including spatial data and creating the final data set. Orange visual programming application and Python programming language were used to carry out the required data mining activities. Exploratory Data Analysis and clustering analysis were used to gain a good understanding of the features in the data set and their relationships. Graph Data Science techniques such as Knowledge Graphs and graph-based clustering were used to gain further insights. Machine Learning regression algorithms such as Multi-Linear Regression, k-Nearest Neighbors and Random Forest were used to train machine learning models to predict average monthly barrels of saltwater disposed in a well. Model performance was optimized using the RMSE metric and the Random Forest model was selected as the final model for deployment to predict performance of a planned SWD well. A multi-target regression model was trained using deep neural network to predict water production in oil and gas wells drilled in the McKenzie county of North Dakota

Utilizing graph-based representation of text in a hybrid approach to multiple documents summarization

The aim of automatic text summarization is to process text with the purpose of identifying and presenting the most important information appearing in the text. In this research, we aim to investigate automatic multiple document summarization using a hybrid approach of extractive and “shallow abstractive methods. We aim to utilize the graph-based representation approach proposed in [1] and [2] as part of our method to multiple document summarization aiming to provide concise, informative and coherent summaries. We start by scoring sentences based on significance to extract top scoring ones from each document of the set of documents being summarized. In this step, we look into different criteria of scoring sentences, which include: the presence of highly frequent words of the document, the presence of highly frequent words of the set of documents and the presence of words found in the first and last sentence of the document and the different combination of such features. Upon running our experiments we found that the best combination of features to use is utilizing the presence of highly frequent words of the document and presence of words found in the first and last sentences of the document. The average f-score of those features had an average of 7.9% increase to other features\u27 f-scores. Secondly, we address the issue of redundancy of information through clustering sentences of same or similar information into one cluster that will be compressed into one sentence, thus avoiding redundancy of information as much as possible. We investigated clustering the extracted sentences based on two criteria for similarity, the first of which uses word frequency vector for similarity measure and the second of which uses word semantic similarity. Through our experiment, we found that the use of the word vector features yields much better clusters in terms of sentence similarity. The word feature vector had a 20% more number of clusters labeled to contain similar sentences as opposed to those of the word semantic feature. We then adopted a graph-based representation of text proposed in [1] and [2] to represent each sentence in a cluster, and using the k-shortest paths we found the shortest path to represent the final compressed sentence and use it as a final sentence in the summary. Human evaluator scored sentences based on grammatical correctness and almost 74% of 51 sentences evaluated got a perfect score of 2 which is a perfect or near perfect sentence. We finally propose a method for scoring the compressed sentences according to the order in which they should appear in the final summary. We used the Document Understanding Conference dataset for year 2014 as the evaluating dataset for our final system. We used the ROUGE system for evaluation which stands for Recall-Oriented Understudy for Gisting Evaluation. This system compare the automatic summaries to “ideal human references. We also compared our summaries ROUGE scores to those of summaries generated using the MEAD summarization tool. Our system provided better precision and f-score as well as comparable recall scores. On average our system has a percentage increase of 2% for precision and 1.6% increase in f-score than those of MEAD while MEAD has an increase of 0.8% in recall. In addition, our system provided more compressed version of the summary as opposed to that generated by MEAD. We finally ran an experiment to evaluate the order of sentences in the final summary and its comprehensibility where we show that our ordering method produced a comprehensible summary. On average, summaries that scored a perfect score in term of comprehensibility constitute 72% of the evaluated summaries. Evaluators were also asked to count the number of ungrammatical and incomprehensible sentences in the evaluated summaries and on average they were only 10.9% of the summaries sentences. We believe our system provide a \u27shallow abstractive summary to multiple documents that does not require intensive Natural Language Processing.