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.

/pdf/sentiment-analysis-and-opinion-mining-8314pykwrf.pdf

Sentiment Analysis and Opinion Mining

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

The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.

Temporal Information Extraction by Predicting Relative Time-lines

Multimodal information fusion at both the signal and semantics level is a core part of most multimedia applications, including indexing, retrieval, and summarization. Prototype systems have implemented early or late fusion of modality-specific processing results through various methodologies including rule-based approaches, informationtheoretic models, and machine learning.1 Vision and language are two of the predominant modalities that are fused, with a long history of results in TRECVid, ImageClef, and other international challenges. During the last decade, vision– language semantic integration has attracted attention from traditionally non-interdisciplinary research communities such as computer vision and natural language processing. This is due to the fact that one modality can greatly assist in processing another by providing cues for disambiguation, complementary information, and noise/error filtering. Recent advances in deep learning have opened up new opportunities in joint modeling of visual and co-occurring verbal information in multimedia.

https://lirias.kuleuven.be/bitstream/123456789/556224/1/MoensetalACMMM2016.pdf

Vision and language integration meets multimedia fusion

We present an architecture that generates medical texts while learning an informative, continuous representation with discriminative features. During training the input to the system is a dataset of captions for medical X-Rays. The acquired continuous representations are of particular interest for use in many machine learning techniques where the discrete and high-dimensional nature of textual input is an obstacle. We use an Adversarially Regularized Autoencoder to create realistic text in both an unconditional and conditional setting. We show that this technique is applicable to medical texts which often contain syntactic and domain-specific shorthands. A quantitative evaluation shows that we achieve a lower model perplexity than a traditional LSTM generator.

Generating Continuous Representations of Medical Texts

https://lirias.kuleuven.be/bitstream/123456789/371064/1/VDTM_Xlite_Final.pdf

Probabilistic topic modeling in multilingual settings: a short overview of its methodology with applications

Today’s content including user generated content is increasingly found in multimedia format. It is known that speech data are sometimes incorrectly transcribed especially when they are spoken by voices on which the transcribers have not been trained or when they contain unfamiliar words. A familiar mining tasks that helps in storage, indexing and retrieval is automatic classification with predefined category labels. Although state-of-the-art classifiers like neural networks, support vector machines (SVM) and logistic regression classifiers perform quite satisfactory when categorizing written text, their performance degrades when applied on speech data transcribed by automatic speech recognition (ASR) due to transcription errors like insertion and deletion of words, grammatical errors and words that are just transcribed wrongly. In this paper, we show that by incorporating content from related written sources in the training of the classification model has a benefit. We especially focus on and compare different representations that make this integration possible, such as representations of speech data that embed content from the written text and simple concatenation of speech and written content. In addition, we qualitatively demonstrate that these representations to a certain extent indirectly correct the transcription noise.

Marie-Francine Moens

Papers

Temporal Information Extraction by Predicting Relative Time-lines

Vision and language integration meets multimedia fusion

Generating Continuous Representations of Medical Texts

Probabilistic topic modeling in multilingual settings: a short overview of its methodology with applications

Using Related Text Sources to Improve Classification of Transcribed Speech Data