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

The research described here is aimed at unresolved problems in both natural language generation and natural language interfaces to database systems. How relevant information is selected and then organized for the generation of responses to questions about database structure is examined. Due to limited space, this paper reports on only one method of explanation, called "compare and contrast". In particular, it describes a specific constraint on relevancy and organization that can be used for this response type.

Generating relevant explanations: natural language responses to questions about database structure

Concomitantly with recent advances in speech coding, recognition and production, parallel computer systems are now commonplace delivering raw computing power measured in hundreds of MIPS and Megaflops. It seems inevitable that within the next decade or so, gigaflop parallel processors will be achievable at modest cost. Indeed, gigaflops per cubic foot is now becoming a standard of measure for parallel computers.

Speech recognition in parallel

We propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative This is intended to be the basis of a story bank resource that would facilitate the automatic analysis of narrative structure and content The methodology calls for annotators to construct propositions that approximate a reference text, by selecting predicates and arguments from among controlled vocabularies drawn from resources such as WordNet and VerbNet Annotators then integrate the propositions into a conceptual graph that maps out the entire discourse; the edges represent temporal, causal and other relationships at the level of story content Because annotators must identify the recurring objects and themes that appear in the text, they also perform coreference resolution and word sense disambiguation as they encode propositions We describe a collection experiment and a method for determining inter-annotator agreement when multiple annotators encode the same short story Finally, we describe ongoing work toward extending the method to integrate the annotators interpretations of character agency (the goals, plans and beliefs that are relevant, yet not explictly stated in the text)

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Building a Bank of Semantically Encoded Narratives

We demonstrate the value of using context in a new-information detection system that achieved the highest precision scores at the Text Retrieval Conference's Novelty Track in 2004. In order to determine whether information within a sentence has been seen in material read previously, our system integrates information about the context of the sentence with novel words and named entities within the sentence, and uses a specialized learning algorithm to tune the system parameters.

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Context and Learning in Novelty Detection

Language generation systems have used a variety of grammatical formalisms for producing syntactic structure and yet, there has been little research evaluating the formalisms for the specifics of the generation task. In our work at Columbia we have primarily used a unification based formalism, a functional Unification Grammar (FUG) and have found it well suited for many of the generation tasks we have addressed. FUG turned out to be better suited for generation than various off-the-shelf parsing formalisms (including an Augmented Transition Network, a Bottom-Up Chart Parser, and a declarative Clause Grammar) that we have also used over the past 5 years. In this paper, we identify the characteristics of FUG that we find useful for generation and focus on order of decision making and its impact on expressions of constraints. Our claim is that order of decision making in FUG through unification allows for flexible interaction between constraints, which, in turn, allows for a more concise representation of constraints. To illustrate these properties of FUG, we use a subtask of the lexical choice problem, namely the task of selecting a connective (e.g., but, however, nonetheless, since, because, etc.) to conjoin two input propositions.

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Kathleen R. McKeown

Papers

Generating relevant explanations: natural language responses to questions about database structure

Speech recognition in parallel

Building a Bank of Semantically Encoded Narratives

Context and Learning in Novelty Detection

A Contrastive Evaluation of Functional Unification Grammar for Surface Language Generation: A Case Study in Choice of Connectives