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

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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.

/pdf/sentiment-analysis-and-opinion-mining-8314pykwrf.pdf

Sentiment Analysis and Opinion Mining

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes. The task is technically challenging and practically very useful. For example, businesses always want to find public or consumer opinions about their products and services. Potential customers also want to know the opinions of existing users before they use a service or purchase a product.

/pdf/a-survey-of-opinion-mining-and-sentiment-analysis-3ypfl5jtav.pdf

A Survey of Opinion Mining and Sentiment Analysis

In this paper, we present a novel approach for mining opinions from product reviews, where it converts opinion mining task to identify product features, expressions of opinions and relations between them. By taking advantage of the observation that a lot of product features are phrases, a concept of phrase dependency parsing is introduced, which extends traditional dependency parsing to phrase level. This concept is then implemented for extracting relations between product features and expressions of opinions. Experimental evaluations show that the mining task can benefit from phrase dependency parsing.

https://people.cs.pitt.edu/~huynv/research/aspect-sentiment/Phrase%20dependency%20parsing%20for%20opinion%20mining.pdf

Phrase Dependency Parsing for Opinion Mining

An advanced model that includes new processes is provided for use as a component of an effective disfluency identifier. The disfluency identifier tags edited words in transcribed speech. A speech recognition unit in combination with a part-of-speech tagger, a disfluency identifier, and a parser form a natural language system that helps machines properly interpret spoken utterances.

Natural language processing of disfluent sentences

Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. The task of automatically extracting them have received considerable attention in recent decades. Different from previous studies, which are usually focused on automatically extracting keyphrases from documents or articles, in this study, we considered the problem of automatically extracting keyphrases from tweets. Because of the length limitations of Twitter-like sites, the performances of existing methods usually drop sharply. We proposed a novel deep recurrent neural network (RNN) model to combine keywords and context information to perform this problem. To evaluate the proposed method, we also constructed a large-scale dataset collected from Twitter. The experimental results showed that the proposed method performs significantly better than previous methods.

/pdf/keyphrase-extraction-using-deep-recurrent-neural-networks-on-ti1t3ug7vk.pdf

Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter

Along with the increasing requirements, the hashtag recommendation task for microblogs has been receiving considerable attention in recent years. Various researchers have studied the problem from different aspects. However, most of these methods usually need handcrafted features. Motivated by the successful use of convolutional neural networks (CNNs) for many natural language processing tasks, in this paper, we adopt CNNs to perform the hashtag recommendation problem. To incorporate the trigger words whose effectiveness have been experimentally evaluated in several previous works, we propose a novel architecture with an attention mechanism. The results of experiments on the data collected from a real world microblogging service demonstrated that the proposed model outperforms state-of-the-art methods. By incorporating trigger words into the consideration, the relative improvement of the proposed method over the state-of-the-art method is around 9.4% in the F1-score.

Hashtag recommendation using attention-based convolutional neural network

In this study, we investigate the problem of named entity recognition for tweets. Named entity recognition is an important task in natural language processing and has been carefully studied in recent decades.  Previous named entity recognition methods usually only used the textual content when processing tweets. However, many tweets contain not only textual content, but also images. Such visual information is also valuable in the name entity recognition task. To make full use of textual and visual information, this paper proposes a novel method to process tweets that contain multimodal information. We extend a bi-directional long short term memory network with conditional random fields and an adaptive co-attention network to achieve this task.  To evaluate the proposed methods, we constructed a large scale labeled dataset that contained multimodal tweets. Experimental results demonstrated that the proposed method could achieve a better performance than the previous methods in most cases.

https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16432/16127

Qi Zhang

Papers

Phrase Dependency Parsing for Opinion Mining

Natural language processing of disfluent sentences

Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter

Hashtag recommendation using attention-based convolutional neural network

Adaptive Co-attention Network for Named Entity Recognition in Tweets