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

The extraction of spatial semantics is important in many real-world applications such as geographical information systems, robotics and navigation, semantic search, etc. Moreover, spatial semantics are the most relevant semantics related to the visualization of language. The goal of multimodal spatial role labeling task is to extract spatial information from free text while exploiting accompanying images. This task is a multimodal extension of spatial role labeling task which has been previously introduced as a semantic evaluation task in the SemEval series. The multimodal aspect of the task makes it appropriate for the CLEF lab series. In this paper, we provide an overview of the task of multimodal spatial role labeling. We describe the task, sub-tasks, corpora, annotations, evaluation metrics, and the results of the baseline and the task participant.

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CLEF 2017: Multimodal Spatial Role Labeling (mSpRL) Task Overview

Presently, we are confronted with an enormous amount of legal documents, which are increasingly recorded in electronic format. There is a need to make the information in legal texts easily and automatically accessible. In this paper we argue that in the legal field, where we are confronted with specific text types, knowledge about discourse structures and the linguistic cues that signal them is very valuable to incorporate in information extraction systems and in text processing systems in general. We also demonstrate the need for adequate formalisms for representing discourse patterns. However, intertextual analysis of texts that describes and explains the properties of text types and genres is underdeveloped in the legal field.

Intelligent Information Extraction from Legal Texts

We propose a novel associative approach for bilingual word lexicon extraction (BLE) from parallel corpora that relies on the paradigm of data reduction instead of data augmentation. The key insight of the approach is the effective usage of sub-corpora sampling and properties of low-frequency words in the task of lexicon induction, particularly in a setting where only limited parallel data are available. Word translation pairs are extracted from many smaller sub-corpora (sampled from the original corpus) according to several frequency-based criteria of similarity. We prove the validity of our data sampling approach, and show that this method outperforms IBM Model 1 and associative methods based on similarity scores and hypothesis testing in terms of precision and F-measure in the task of lexicon extraction. Additionally, we show that our sampling-based method can learn correct word translations from fewer data. TITLE AND ABSTRACT IN ANOTHER LANGUAGE (CROATIAN) Uzorkovanje Potkorpusa uz Primjenu u Ekstrakciji Dvojezicnih Rjecnika U radu se predlaže nov asocijativan pristup ekstrakciji dvojezicnih rjecnika iz usporednih korpusa koji se oslanja na paradigmu smanjivanja kolicine podataka umjesto njezinog povecavanja. Kljucna je ideja pristupa ucinkovita uporaba uzorkovanja potkorpusa te svojstava niskofrekventnih rijeci u zadatku indukcije rjecnika, posebice u situacijama kada je na raspolaganju ogranicen skup usporednih podataka. Prijevodni parovi rijeci ekstrahirani su iz veceg broja manjih potkorpusa (uzorkovanih iz izvornog korpusa) temeljem nekoliko frekvencijski utemeljenih kriterija slicnosti. U radu je pokazana ispravnost naseg pristupa temeljenog na uzorkovanju potkorpusa. Pokazano je da ovaj postupak u smislu F-mjere na zadatku ekstrakcije leksikona nadmasuje IBM-ov Model 1 te asocijativne postupke temeljene na ocjenama slicnosti i testiranju hipoteze. Takoder je pokazano da nas postupak temeljen na uzorkovanju može nauciti ispravne prijevode rijeci iz manjih kolicina podataka.

Sub-corpora Sampling with an Application to Bilingual Lexicon Extraction

In this paper we report on our experiments on linking names and faces as found in images and captions of online news websites. Whereas previously, the focus has been mostly on assigning names to the faces, we generalize this framework, exploiting the (a)symmetry between the visual and textual modalities. This leads to different schemes for assigning names to faces, assigning faces to names, and establishing name-face link pairs. On top of that, we investigate the use of textual and visual structural information to predict the presence of the corresponding entity in the other modality. This further improves the accuracy of the cross-media linking results.

/pdf/linking-names-and-faces-seeing-the-problem-in-different-ways-1bh8s1eorw.pdf

Linking names and faces: seeing the problem in different ways

In this paper we study cross-language information retrieval using a bilingual topic model trained on comparable corpora such as Wikipedia articles. The bilingual Latent Dirichlet Allocation model (BiLDA) creates an interlingual representation, which can be used as a translation resource in many different multilingual settings as comparable corpora are available for many language pairs. The probabilistic interlingual representation is incorporated in a statistical language model for information retrieval. Experiments performed on the English and Dutch test datasets of the CLEF 2001-2003 CLIR campaigns show the competitive performance of our approach compared to cross-language retrieval methods that rely on pre-existing translation dictionaries that are hand-built or constructed based on parallel corpora.

/pdf/cross-language-information-retrieval-with-latent-topic-3z43i5zbnq.pdf

Marie-Francine Moens

Papers

CLEF 2017: Multimodal Spatial Role Labeling (mSpRL) Task Overview

Intelligent Information Extraction from Legal Texts

Sub-corpora Sampling with an Application to Bilingual Lexicon Extraction

Linking names and faces: seeing the problem in different ways

Cross-language information retrieval with latent topic models trained on a comparable corpus