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

/pdf/foundations-of-statistical-natural-language-processing-1ou5e6c2q6.pdf

Foundations of Statistical Natural Language Processing

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

From the Publisher:
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

/pdf/speech-and-language-processing-an-introduction-to-natural-atzaecqfem.pdf

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

語用論(Pragmatics)を考える

In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.

/pdf/twitter-sentiment-analysis-the-good-the-bad-and-the-omg-x5ixw1ra4n.pdf

Twitter Sentiment Analysis: The Good the Bad and the OMG!

Proceedings of the Conference on Human Factors in Computing Systems

To participate in a dialogue a system must be capable of reasoning about its own previous utterances. Follow-up questions must be interpreted in the context of the ongoing conversation, and the system's previous contributions form part of this context. Furthermore, if a system is to be able to clarify misunderstood explanations or to elaborate on prior explanations, it must understand what it has conveyed in prior explanations. Previous approaches to generating multisentential texts have relied solely on rhetorical structuring techniques. In this paper, we argue that, to handle explanation dialogues successfully, a discourse model must include information about the intended effect of individual parts of the text on the hearer, as well as how the parts relate to one another rhetorically. We present a text planner that records this information and show how the resulting structure is used to respond appropriately to a follow-up question.

/pdf/planning-text-for-advisory-dialogues-capturing-intentional-1y9b7edcud.pdf

Planning text for advisory dialogues: capturing intentional and rhetorical information

Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization

Rhetorical Structure Theory (RST) (Mann and Thompson 1987), argues that in most coherent discourse, consecutive discourse elements are related by a small set of rhetorical relations. Moreover, RST suggests that the information conveyed in a discourse over and above what is conveyed in its component clauses can be derived from the rhetorical relation-based structure of the discourse. A large number of natural language generation systems rely on the rhetorical relations defined in RST to impose structure on multi-sentential text (Hovy 1991; Knott 1991; Moore and Paris 1989; Rosner and Stede 1992). In addition, many descriptive studies of discourse have employed RST (Fox 1987; Linden, Cumming, and Martin 1992; Matthiessen and Thompson 1988). However, recent work by Moore and Paris (1992) noted that RST cannot be used as the sole means of controlling discourse structure in an interactive dialogue system, because RST representations provide insufficient information to support the generation of appropriate responses to "follow-up questions." The basic problem is that an RST representation of a discourse does not fully specify the intentional structure (Grosz and Sidner 1986) of that discourse. Intentional structure is crucial for responding effectively to questions that address a previous utterance: without a record of what an utterance was intended to achieve, it is impossible to elaborate or clarify that utterance. 1 Further consideration has led us to conclude that the difficulty observed by Moore and Paris stems from a more fundamental problem with RST analyses. RST presumes that, in general, there will be a single, preferred rhetorical relation holding between consecutive discourse elements. In fact, as has been noted in other work on discourse structure (Grosz and Sidner 1986), discourse elements are related simultaneously on multiple levels. In this paper, we focus on two levels of analysis. The first involves the relation between the information conveyed in consecutive elements of a coherent discourse. Thus, for example, one utterance may describe an event that can be presumed to be the cause of another event described in the subsequent utterance. This causal relation is at what we will call the informational level. The second level of relation results from the fact that discourses are produced to effect changes in the mental state of the discourse participants. In coherent discourse, a speaker is carrying out a consistent plan to achieve the intended changes, and consecutive discourse elements are related to one another by means of the ways in which they participate in that plan. Thus, one utterance may be intended to increase the likelihood that the hearer will come to

/pdf/a-problem-for-rst-the-need-for-multi-level-discourse-1un2ja3ml8.pdf

Johanna D. Moore

Papers

Twitter Sentiment Analysis: The Good the Bad and the OMG!

Proceedings of the Conference on Human Factors in Computing Systems

Planning text for advisory dialogues: capturing intentional and rhetorical information

Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization

A problem for RST: the need for multi-level discourse analysis