Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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.

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

We present a simple and effective approach to complement search results for children's web queries with child-oriented multimedia results, such as coloring pages and music sheets. Our approach determines appropriate media types for a query by searching Google's database of frequent queries for co-occurrences of a query's terms (e.g., "dinosaurs") with preselected multimedia terms (e.g., "coloring pages"). We show the effectiveness of this approach through an online user evaluation.

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A picture is worth a thousand search results: finding child-oriented multimedia results with collAge

Effective retrieval of court decisions is important. Automatically identifying legal concepts in the decision texts would be very helpful. In this paper we investigate how a statistics for hypothesis testing, i.e., the likelihood ratio, can help in this task. We describe how this statistic can be used for detecting important multi-term phrases in the case texts, how it can be used to find correlated terms, and how it is a means for feature or topic signature selection in automated case categorization. The technology has been tested upon more than 600 US cases.

Concept extraction from legal cases: the use of a statistic of coincidence

The MOSAIC project investigates a retrieval model for court decisions based on structured and unstructured (natural language) information in legal cases This paper focuses on how relevant information in court decisions can function as a key for retrieval and on the automated construction of case representations Techniques of automated concept learning and rhetorical structure identification are among the most promising ones

First steps in building a model for the retrieval of court decisions

We present rpref ; our generalization of the bpref evaluation metric for assessing the quality of search engine results, given graded rather than binary user relevance judgments.

Rpref: a generalization of Bpref towards graded relevance judgments

We propose a method for adapting Semantic Role Labeling (SRL) systems from a source domain to a target domain by combining a neural language model and linguistic resources to generate additional training examples. We primarily aim to improve the results of Location, Time, Manner and Direction roles. In our methodology, main words of selected predicates and arguments in the source-domain training data are replaced with words from the target domain. The replacement words are generated by a language model and then filtered by several linguistic filters (including Part-Of-Speech (POS), WordNet and Predicate constraints). In experiments on the out-of-domain CoNLL 2009 data, with the Recurrent Neural Network Language Model (RNNLM) and a well-known semantic parser from Lund University, we show enhanced recall and F1 without penalizing precision on the four targeted roles. These results improve the results of the same SRL system without using the language model and the linguistic resources, and are better than the results of the same SRL system that is trained with examples that are enriched with word embeddings. We also demonstrate the importance of using a language model and the vocabulary of the target domain when generating new training examples.

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Marie-Francine Moens

Papers

A picture is worth a thousand search results: finding child-oriented multimedia results with collAge

Concept extraction from legal cases: the use of a statistic of coincidence

First steps in building a model for the retrieval of court decisions

Rpref: a generalization of Bpref towards graded relevance judgments

Domain adaptation in semantic role labeling using a neural language model and linguistic resources