Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

/pdf/opinion-mining-and-sentiment-analysis-3v5ya3qqiw.pdf

Opinion Mining and Sentiment Analysis

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

Photo tourism: exploring photo collections in 3D

The Web is a useful data source for knowledge extraction, as it provides diverse content virtually on any possible topic. Hence, a lot of research has been recently done for improving mining in the Web. However, relatively little research has been done taking directly into account the temporal aspects of the Web. In this chapter, we analyze data stored in Web archives, which preserve content of the Web, and investigate the methodology required for successful knowledge discovery from this data. We call the collection of such Web archives past Web; a temporal structure composed of the past copies of Web pages. First, we discuss the character of the data and explain some concepts related to utilizing the past Web, such as data collection, analysis and processing. Next, we introduce examples of two applications, temporal summarization and a browser for the past Web. IntroductIon As the Web changes continuously, it is necessary to preserve the past content of pages for a future reuse. The Internet Archive1 is the best-known and largest public Web archive containing data crawled since 1996. Other Web archives exist, for example, ones containing Web pages from particular countries (e.g., Arvidson, Persson, & Mannerheim, 2000; Hallgrimsson & Bang, 2003). Besides, there are also numerous repositories of past copies of pages such as caches, site archives, personal page repositories or search engine caches. Web archives provide a view on the history of the Web reflecting past societal states. Past content of pages can reveal the histories of underlying elements represented by these pages, such as institutions, companies, people or other entities. For example, one could approximately detect when a particular member left some laboratory by detecting the time point at which her or his name was removed from the list of laboratory’s personnel. In general, the use DOI: 10.4018/978-1-59904-576-4.ch017

Utilizing Past Web for Knowledge Discovery

Office applications are becoming a major pillar of today’s organizations since they are used to edit a vast amount of digital documents. Finding these office documents in large databases that fit users’ needs is becoming increasingly important. Traditional search tools that employ keyword and phrase matching between the query and search index alone tend to offer high recall and low precision. The search users are faced with too many irrelevant results. In order to solve this problem, we propose a novel search system that effectively searches the target documents by the search query whose definition is based on the document type, search terms and the semantic relationship between the search terms and the target documents. We present a technique that collects search terms and their semantic relationship from the documents of some office applications to generate the XML-based search indices that can effectively locate the office documents. Furthermore, our query optimization algorithm is also presented.

An Effective Document Search Technique by Semantic Relationship Approach

This paper proposes to establish a collaborative environment to achieve enhanced information access on the small-form-factor devices. We designed a distributed user interface that crosses devices to cooperatively present information adaptable to small displays. We applied our system to the document and webpage that are ubiquitously available media content on mobile devices.

/pdf/a-collaborative-environment-for-enhanced-information-access-24t62mqee1.pdf

A collaborative environment for enhanced information access on small-form-factor devices

Detecting intent in Web search activity is important task for finding relevant Web information. However extracting intents from users' queries is difficult as users express their intent by issuing short and often ambiguous queries, yet at the same time it is crucial factor for enhancing user satisfaction. Showing the variety of candidate intents behind a query could help users choose correct intent expressions and improve the Web search. In this paper, we propose the methodology for detecting intent of Web queries using Community Question-Answer (CQA)information. Our assumption is that questions and its answers in CQA corpus reflect intents of questioners. To detect these intents, we use the semantic connections between questions and its answers. We categorize questions to find the connections of features within a question and its answers, detect intent words in answers by calculating supports of concerned CQA contents, and cluster questions and their answers by these intent words. Experimental results show that the variety of Web query intents can be found with satisfactory performance.

Detecting Intent of Web Queries Using Questions and Answers in CQA Corpus

Digital libraries are currently growing rapidly in number and size, covering various fields. However, despite the great deal of intellectual effort and large budgets necessary for their construction, digital libraries are often limited to use by specialists. We describe a framework that will attract more users to digital libraries through enhancing the presentation layers. We propose the guiding principle to such enhancement, the Sightseeing Metaphor Framework (SF), which is a structure based on user activities during sightseeing. We exemplify it in a Web-based regional information presentation system. The evaluation we did for an elementary school classroom revealed that the system created a better impression on the students compared to an existing presentation scheme.

Katsumi Tanaka

Papers

Utilizing Past Web for Knowledge Discovery

An Effective Document Search Technique by Semantic Relationship Approach

A collaborative environment for enhanced information access on small-form-factor devices

Detecting Intent of Web Queries Using Questions and Answers in CQA Corpus

Traveling in digital archive world: sightseeing metaphor framework for enhancing user experiences in digital libraries