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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

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.

/pdf/opinion-mining-and-sentiment-analysis-3v5ya3qqiw.pdf

Opinion Mining and Sentiment Analysis

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering --- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

Neural Collaborative Filtering

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.

A Survey of Location Prediction on Twitter

Social tags describe images from many aspects including the visual content observable from the images, the context and usage of images, user opinions and others Not all tags are therefore useful for image search and are appropriate for tag recommendation with respect to visual content of images However, the relationship between a given tag and the visual content of its tagged images are largely ignored in existing studies on tags and in tagging applications In this paper, we bridge the two orthogonal areas of social image tagging and query performance prediction in Web search, to quantify tag representativeness of the visual content presented in the annotated images, which is also known as tag visual-representativeness In simple words, tag visual-representativeness characterizes the effectiveness of a tag in describing the visual content of the set of images annotated by the tag A tag is visually representative if its annotated images are visually similar to each other, containing a common visual concept such as an object or a scene We propose two distance metrics, namely cohesion and separation, to quantify tag visual-representativeness from the set of images annotated by a tag and the entire image collection Through extensive experiments on a subset of Flickr images, we demonstrate the characteristics of seven variants of the distance metrics derived from different low-level image representations and show that the visually representative tags can be identified with high precision Importantly, these proposed distance measures are parameter free with linear or constant computational complexity, thus are effective for practical applications

/pdf/quantifying-tag-representativeness-of-visual-content-of-33zqjyt5mq.pdf

Quantifying tag representativeness of visual content of social images

Geographical inquiry involves collecting, using and making sense of the data to investigate some geographical phenomena. With the increasing number of mobile devices equipped with Internet access capabilities, there is a wide scope for using it in field inquiry where learning can take place in the form of social interactions between team members while performing a common task. The authors examine the use of MobiTOP (Mobile Tagging of Objects and People), a geospatial digital library system which allows users to contribute and share multimedia annotations via mobile devices, within a geographical field inquiry. A key feature of MobiTOP that is well suited for collaborative learning is that annotations are hierarchical, allowing annotations to be annotated by other users to an arbitrary depth. A group of student and teachers involved in an inquiry-based learning activity in geography was instructed to identify rock types and associated landforms by collaborating with each other using the MobiTOP system. A m...

Lessons from Learner Experiences in a Field-Based Inquiry in Geography Using Mobile Devices

Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence. In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. We then refine this graph to select important words for relation prediction. Finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction. Specifically, in our proposed GDPNet (Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph Generator (GGG) to generate edges of the multi-view graph. The graph is then refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE.

GDPNet: Refining Latent Multi-View Graph for Relation Extraction

Social influence analysis in online social networks is the study of people's influence by analyzing the social interactions between individuals. There have been increasing research efforts to understand the influence propagation phenomenon due to its importance to information dissemination among others. Despite the progress achieved by state-of-the-art social influence analysis techniques, a key limitation of these techniques is that they only utilize positive interactions (e.g., agreement, trust) between individuals, ignoring two equally important factors, namely, negative relationships (e.g., distrust, disagreement) between individuals and conformity of people, which refers to a person's inclination to be influenced. In this paper, we propose a novel algorithm CASINO (Conformity-Aware Social INfluence cOmputation) to study the interplay between influence and conformity of each individual. Given a social network, CASINO first extracts a set of topic-based subgraphs where each subgraph depicts the social interactions associated with a specific topic. Then it optionally labels the edges (relationships) between individuals with positive or negative signs. Finally, it computes the influence and conformity indices of each individual in each signed topic-based subgraph. Our empirical study with several real-world social networks demonstrates superior effectiveness and accuracy of CASINO compared to state-of-the-art methods. Furthermore, we revealed several interesting characteristics of "influentials" and "conformers" in these networks.

Aixin Sun

Papers

A Survey of Location Prediction on Twitter

Quantifying tag representativeness of visual content of social images

Lessons from Learner Experiences in a Field-Based Inquiry in Geography Using Mobile Devices

GDPNet: Refining Latent Multi-View Graph for Relation Extraction

CASINO: towards conformity-aware social influence analysis in online social networks