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

From the Publisher:
Written by the author of the best-selling HyperText & HyperMedia, this book provides an excellent guide to the methods of usability engineering. Special features: emphasizes cost-effective methods that will help developers improve their user interfaces immediately, shows you how to avoid the four most frequently listed reasons for delay in software projects, provides step-by-step information about which methods to use at various stages during the development life cycle, and offers information on the unique issues relating to informational usability. You do not need to have previous knowledge of usability to implement the methods provided, yet all of the latest research is covered.

Usability Engineering

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Statistical approaches to processing natural language text have become dominant in recent years This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear The book contains all the theory and algorithms needed for building NLP tools It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications

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Foundations of Statistical Natural Language Processing

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

Aspectual classification maps verbs to a small set of primitive categories in order to reason about time This classification is necessary for interpreting temporal modifiers and assessing temporal relationships, and is therefore a required component for many natural language applicationsA verb's aspectual category can be predicted by co-occurrence frequencies between the verb and certain linguistic modifiers These frequency measures, called linguistic indicators, are chosen by linguistic insights However, linguistic indicators used in isolation are predictively incomplete, and are therefore insufficient when used individuallyIn this article, we compare three supervised machine learning methods for combining multiple linguistic indicators for aspectual classification: decision trees, genetic programming, and logistic regression A set of 14 indicators are combined for classification according to two aspectual distinctions This approach improves the classification performance for both distinctions, as evaluated over unrestricted sets of verbs occurring across two corpora This demonstrates the effectiveness of the linguistic indicators and provides a much-needed full-scale method for automatic aspectual classification Moreover, the models resulting from learning reveal several linguistic insights that are relevant to aspectual classification We also compare supervised learning methods with an unsupervised method for this task

/pdf/learning-methods-to-combine-linguistic-indicators-improving-580j0qhg4n.pdf

Learning methods to combine linguistic indicators: improving aspectual classification and revealing linguistic insights

https://academiccommons.columbia.edu/doi/10.7916/D8417705/download

Customization in a unified framework for summarizing medical literature

Summaries typically convey maximal information in minimal space. In this paper, we describe an approach to summary generation that opportunistically folds information from multiple facts into a single sentence using concise linguistic constructions. Unlike previous work in generation, how information gets added into a summary depends in part on constraints from how the text is worded so far. This approach allows the construction of concise summaries, containing complex sentences that pack in information. The resulting summary sentences are, in fact, longer than sentences generated by previous systems. We describe two applications we have developed using this approach, one of which produces summaries of basketball games (STREAK) while the other (PLANDOC) produces summaries of telephone network planning activity; both systems summarize input data as opposed to full text. The applications implement opportunistic summary generation using complementary approaches. STREAK uses revision, creating a draft of essential facts and then using revision rules constrained by the draft wording to add in additional facts as the text allows. PLANDOC uses discourse planning, looking ahead in its text plan to group together facts which can be expressed concisely using conjunction and deleting repetitions. In this paper, we describe the problems for summary generation, the two domains, the linguistic constructions that the systems use to convey information concisely and the textual constraints that determine what information gets included.

Generating concise natural language summaries

We propose a new algorithm called DK-vec for aligning pairs of Asian/Indo-European noisy parallel texts without sentence boundaries. DK-vec improves on previous alignment algorithms in that it handles better the non-linear nature of noisy corpora. The algorithm uses frequency, position and recency information as features for pattern matching. Dynamic Time Warping is used as the matching technique between word pairs. This algorithm produces a small bilingual lexicon which provides anchor points for alignment.

Aligning Noisy Parallel Corpora Across Language Groups : Word Pair Feature Matching by Dynamic Time Warping

We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of crosslingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct crosslingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.

Kathleen R. McKeown

Papers

Learning methods to combine linguistic indicators: improving aspectual classification and revealing linguistic insights

Customization in a unified framework for summarizing medical literature

Generating concise natural language summaries

Aligning Noisy Parallel Corpora Across Language Groups : Word Pair Feature Matching by Dynamic Time Warping

WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization