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.

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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

Many homemakers feel stressed when deciding on the menu of the day [?]. Even though they have a vague idea of some dishes, it is not easy for them to make the idea clear. Therefore, this research proposes a system that finds recipes corresponding to the user's vague requirements. On the Web, many blog-type recipes describe not only a recipe itself, but also the reasons why a given recipe were created or selected by the page authors. Firstly, the system extracts the author's reasons for the creation or selection of a recipe from the blog-type recipes. Secondly, the system lets the user input his/her present situation or feelings which must include his/her vague requirement and mine his/her reasons for recipe selection from the input. Finally, the system outputs recipes that meet the user's vague requirements by associating the reasons for recipe selection from the user's input text with the reasons accompanying the blog-type recipes.

Recipe search for blog-type recipe articles based on a user's situation

Relevance has so far been a major evaluation criterion in information retrieval. Treating context of search results as first-class user information needs, we explore a new direction of search in the light of information comprehension, which is a new dimension of relevance criteria. In this paper, we argue that it is entailment relations that enable the modeling of media comprehension at the conceptual level. We also propose a method to construct an entailment ontology for e-learning and show its application to lecture material retrieval, which aims to identify, for user queries, not only precise search targets inside media streams but also their surrounding context for enhancing comprehension (of the search targets).

Construction of an Entailment Ontology for Enhancing Comprehension of Search Results Inside e-Learning Materials

Search engines return a huge number of Web search results, and the user usually checks merely the top 5 or 10 results. However, the user sometimes must collect information exhaustively such as collecting all the publications which a certain person had written, or gathering a lot of useful information which assists the user to buy. In this case, the user must repeatedly check search results that are clearly irrelevant. We believe that people would use a search system which provides the reranking or classifying functions by the user's interaction. We have already proposed a reranking system based on the user's edit-and-propagate operations. In this paper, we introduce the drag-and-drop operation into our system to support the user's exhaustive search.

Reranking and Classifying Search Results Exhaustively Based on Edit-and-Propagate Operations

In this paper, methods for ranking coordinate terms and hypernyms of a given query according to their appropriateness are proposed. Although previous studies have proposed methods for discovering coordinate terms or hypernyms of a query, they focused on only discovering such terms and evaluating discovered terms based on a binary evaluation: appropriate or inappropriate. Unlike these studies, we rank coordinate terms and hypernyms of a query and evaluate the terms by considering their appropriateness. In the proposed method, a bipartite graph is created based on hypernyms of a query and hyponyms of each hypernym using a hypernym-hyponym dictionary. Subsequently, we apply a HITS-based algorithm to the bipartite graph and rank coordinate terms and hypernyms based on their appropriateness. The experimental results obtained using 50 queries demonstrate that our method could rank appropriate coordinate terms and hypernyms higher than other comparable methods.

Ranking of Coordinate Terms and Hypernyms Using a Hypernym-Hyponym Dictionary

To address the difficulty in clipping articles from various mobile applications (apps), we propose a novel framework called UniClip, which allows a user to snap a screen of an article to save the whole article in one place. The key task of the framework is search by screenshots, which has three challenges: (1) how to represent a screenshot; (2) how to formulate queries for effective article retrieval; and (3) how to identify the article from search results. We solve these by (1) segmenting a screenshot into structural units called blocks, (2) formulating effective search queries by considering the role of each block, and (3) aggregating the search result lists of multiple queries. To improve efficiency, we also extend our approach with learning-to-rank techniques so that we can find the desired article with only one query. Experimental results show that our approach achieves high retrieval performance (F1 = 0.868), which outperforms baselines based on keyword extraction and chunking methods. Learning-to-rank models improve our approach without learning by about 6%. A user study conducted to investigate the usability of UniClip reveals that ours is preferred by 21 out of 22 participants for its simplicity and effectiveness.

Katsumi Tanaka

Papers

Recipe search for blog-type recipe articles based on a user's situation

Construction of an Entailment Ontology for Enhancing Comprehension of Search Results Inside e-Learning Materials

Reranking and Classifying Search Results Exhaustively Based on Edit-and-Propagate Operations

Ranking of Coordinate Terms and Hypernyms Using a Hypernym-Hyponym Dictionary

Search by Screenshots for Universal Article Clipping in Mobile Apps