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

Longman Student Grammar of Spoken and Written English March 13th, 2019 These tell us what choices are available in the grammar but we also need to understand how these choices are used to create discourse in different situations The year 1999 saw the publication of a large scale grammar of English with the aim of meeting the above needs the Longman ielts house net, longman student grammar of spoken and written english, longman grammar of spoken and written english roffel, longman student grammar of spoken and written english pdf, longman grammar of spoken and written english libros, longmans student grammar of spoken and written english, english longman grammar of spoken and written eng free, longman student grammar of spoken and written english, longman grammar of spoken and written english pdf web, lms2 vu edu pk, longman student grammar of spoken and written english, longman grammar of spoken and written english wikipedia, longman student grammar of spoken and written english, download pdf longman grammar of spoken and written, longman student grammar of spoken and written english, longman grammar of spoken and written english amazon co, longman student grammar of spoken and written english, longman grammar of spoken and written english edoc pub, the languagelab library longman student grammar of, longman grammar of spoken and written english scribd, longman grammar of spoken and written english free, the longman grammar of spoken and written english, longman grammar of spoken and written english epdf tips, grammars of spoken english new outcomes of corpus, longman grammar of spoken and written english tesl ej, book reviews longman grammar of spoken and written english, longman student grammar of spoken and written english, longman grammar of spoken and written english worldcat org, douglas biber et al longman grammar of spoken and, project muse longman grammar of spoken and written, longman grammar of spoken and written english oxford, 9780582237261 longman student grammar of spoken and, longman student grammar of spoken and written english, pdf longman grammar of spoken and written english, longman student grammar of spoken and written english, longman grammar of spoken and written english google books, student grammar of spoken and written english workbook, longman grammar of spoken and written english goodreads, longman student grammar of spoken and written english, longman student grammar of spoken and written english le, longman student grammar of spoken and written english, longman grammar of spoken and written english co construction, longman student grammar of spoken and written english, longman student grammar of spoken and written english by, longman student grammar of spoken and written english workbook, longman grammar of spoken and written english douglas

「会話の文法」に関する一考察 : Longman Grammar of Spoken and Written Englishの場合

Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count

The article examines and analyzes the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians. As for political discourse, it is perhaps the communicator, the linguistic personality, who plays the most important role in the communication. The linguistic personality of a politician is of particular interest in political discourse content-analysis, since it has the greatest influence on the public consciousness via mass media. Using text as a source of psychological and cognitive information has been gaining popularity. Researchers use a variety of methods to analyze texts, but Linguistic Inquiry Word Count (LIWC) has proved to be the most common technique. The analysis of linguistic patterns of political discourse shows that in the context of political speech events such as media interviews, politicians make a unique choice of lexical units, which can be interpreted as a manifestation of certain personality traits. However, despite the significance of the results, there are clear limitations to the use of computerized methodologies to make political discourse content-analysis, such as the limited interpretive capacity of software to understand pragmatic and contextual use of lexical units.

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Political discourse content analysis: a critical overview of a computerized text analysis program linguistic inquiry and word count (liwc)

Based on an extensive literature review, we suggest that ‘fake news’ alludes to two dimensions of political communication: the fake news genre (i.e. the deliberate creation of pseudojournalistic di...

https://www.tandfonline.com/doi/pdf/10.1080/23808985.2019.1602782

Fake news as a two-dimensional phenomenon: a framework and research agenda

The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users’ engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

Combating Fake News: A Survey on Identification and Mitigation Techniques

The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

/pdf/semi-supervised-learning-over-heterogeneous-information-2aztbqljkd.pdf

Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks.

Modeling dynamically-evolving, multi-relational graph data has received a surge of interests with the rapid growth of heterogeneous event data. However, predicting future events on such data requires global structure inference over time and the ability to integrate temporal and structural information, which are not yet well understood. We present Recurrent Event Network (RE-Net), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e.g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events. RE-Net employs a recurrent event encoder to model the temporally conditioned joint probability distribution for the event sequences, and equips the event encoder with a neighborhood aggregator for modeling the concurrent events within a time window associated with each entity. We apply teacher forcing for model training over historical data, and infer graph sequences over future time stamps by sampling from the learned joint distribution in a sequential manner. We evaluate the proposed method via temporal link prediction on five public datasets. Extensive experiments demonstrate the strength of RE-Net, especially on multi-step inference over future time stamps. Code and data can be found at this https URL .

/pdf/recurrent-event-network-global-structure-inference-over-3cn1dz7dcb.pdf

Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph

Sequence labeling is a fundamental task for a range of natural language processing problems. When used in practice, its performance is largely influenced by the annotation quality and quantity, and meanwhile, obtaining ground truth labels is often costly. In many cases, ground truth labels do not exist, but noisy annotations or annotations from different domains are accessible. In this paper, we propose a novel framework Consensus Network (ConNet) that can be trained on annotations from multiple sources (e.g., crowd annotation, cross-domain data). It learns individual representation for every source and dynamically aggregates source-specific knowledge by a context-aware attention module. Finally, it leads to a model reflecting the agreement (consensus) among multiple sources. We evaluate the proposed framework in two practical settings of multi-source learning: learning with crowd annotations and unsupervised cross-domain model adaptation. Extensive experimental results show that our model achieves significant improvements over existing methods in both settings. We also demonstrate that the method can apply to various tasks and cope with different encoders.

/pdf/learning-to-contextually-aggregate-multi-source-supervision-3pbdtrx3hk.pdf

He Jiang

Papers

Combating Fake News: A Survey on Identification and Mitigation Techniques

Combating Fake News: A Survey on Identification and Mitigation Techniques

Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks.

Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph

Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling