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 identification and classification of some circumstance semantic roles like Location, Time, Manner and Direction, a task of Semantic Role Labeling (SRL), plays a very important role in building text understanding applications However, the performance of the current SRL systems on those roles is often very poor, especially when the systems are applied on domains other than the ones they are trained on We present a method to build open domain SRL system, in which the training data is expanded by replacing its predicates by words in the testing domain A language model, which is considered as a text mining technique, and some linguistic resources are used to select from the vocabulary of the testing domain the best words for the replacement We apply our method on the case study of transferring a semantic role labeler trained on the news domain to the children story domain It gives us valuable improvements over the four circumstance semantic roles Location, Time, Manner and Direction

Text mining for open domain semi-supervised semantic role labeling

Legal information is often accessible via portal web sites. Legal documents typically combine structured and unstructured information, the former being tagged with markup languages such as XML (Extensible Markup Language). Current information retrieval research takes into account the structured information content of documents when computing the relevance ranking. Such an approach is very promising for the retrieval of legal documents. This is illustrated with two retrieval models specifically designed for the retrieval of legislation.

Combining Structured and Unstructured Information in a Retrieval Model for Accessing Legislation

Speech data has been established as an extremely rich and important source of information. However, we still lack suitable methods for the semantic annotation of speech that has been transcribed by automated speech recognition (ASR) systems . For instance, the semantic role labeling (SRL) task for ASR data is still an unsolved problem, and the achieved results are significantly lower than with regular text data. SRL for ASR data is a difficult and complex task due to the absence of sentence boundaries, punctuation, grammar errors, words that are wrongly transcribed, and word deletions and insertions. In this paper we propose a novel approach to SRL for ASR data based on the following idea: (1) combine evidence from different segmentations of the ASR data, (2) jointly select a good segmentation, (3) label it with the semantics of PropBank roles. Experiments with the OntoNotes corpus show improvements compared to the state-of-the-art SRL systems on the ASR data. As an additional contribution, we semi-automatically align the predicates found in the ASR data with the predicates in the gold standard data of OntoNotes which is a quite difficult and challenging task, but the result can serve as gold standard alignments for future research.

https://lirias.kuleuven.be/bitstream/123456789/500035/1/ShresthaetalCICLING2015.pdf

Semantic role labeling of speech transcripts

Legislative databases are a well-established information source for the legal professional. However, the manual updating of the texts and their reference data or metadata that are necessary for an effective consultation of the databases is very expensive and does not always guarantee the correctness of the retrieved texts. A valuable solution to the maintenance problems of the databases is more automation in the lifecycle of legislation. This can be achieved by the official electronic publication of legislation including the texts (original and consolidated) and their reference data, and by incorporating intelligent techniques for drafting, indexing and hypertext linking. Such an approach would allow for an automated update of the texts and the reference data in the databases.

Digital legislation: reflections on the Agora-Lex project

Summarizing document texts at various levels of detail is required for many information selection tasks For instance, when loading and visualizing documents on small screens of handheld devices, it is important to be able to dynamically compress texts In this article we discuss a technique of generating hierarchical topic trees of a text and to use them in various ways to build summaries of a flexible length For the topic tree building process we have implemented both a deterministic and probabilistic approach We compare the results when the topic tree is used for automatic summarization

/pdf/summarizing-texts-at-various-levels-of-detail-561e15zjwa.pdf

Marie-Francine Moens

Papers

Text mining for open domain semi-supervised semantic role labeling

Combining Structured and Unstructured Information in a Retrieval Model for Accessing Legislation

Semantic role labeling of speech transcripts

Digital legislation: reflections on the Agora-Lex project

Summarizing texts at various levels of detail