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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

Data Mining - Concepts and Techniques.

On-line construction of suffix trees

Journal Of The American Statistical Association V-27

Sentiment dictionary is of great value to sentiment analysis, which is used widely in sentiment analysis compositionality. However, the sentiment polarity and intensity of the word may vary from one domain to another. In this paper, we introduce a novel approach to build domain-dependent sentiment dictionary, SentiDomain. We propose a weak supervised neural model that aims at learning a set of sentiment clusters embedding from the sentence global representation of the target domain. The model is trained on unlabeled data with weak supervision by reconstructing the input sentence representation from the resulting representation. Furthermore, we also propose an attention-based LSTM model to address aspect-level sentiment analysis task based on the sentiment score retrieved from the proposed dictionary. The key idea is to weight-down the non-sentiment parts among aspect-related information in a given sentence. Our extensive experiments on both English and Chinese benchmark datasets have shown that compared to the state-of-the-art alternatives, our proposals can effectively improve polarity detection.

Constructing domain-dependent sentiment dictionary for sentiment analysis

The state-of-the-art solutions for Aspect-Level Sentiment Analysis (ALSA) were built on a variety of Deep Neural Networks (DNN), whose efficacy depends on large quantities of accurately labeled training data. Unfortunately, high-quality labeled training data usually require expensive manual work, thus may not be readily available in real scenarios. In this paper, we propose a novel approach for aspect-level sentiment analysis based on the recently proposed paradigm of Gradual Machine Learning (GML), which can enable accurate machine labeling without the requirement for manual labeling effort. It begins with some easy instances in a task, which can be automatically labeled by the machine with high accuracy, and then gradually labels the more challenging instances by iterative factor graph inference. In the process of gradual machine learning, the hard instances are gradually labeled in small stages based on the estimated evidential certainty provided by the labeled easier instances. Our extensive experiments on the benchmark datasets have shown that the performance of the proposed solution is considerably better than its unsupervised alternatives, and also highly competitive compared with the state-of-the-art supervised DNN models.

Aspect-level sentiment analysis based on gradual machine learning

Parallelizing maximal clique and k-plex enumeration over graph data

Machine-learning-based entity resolution has been widely studied. However, some entity pairs may be mislabeled by machine learning models and existing studies do not study the risk analysis problem -- predicting and interpreting which entity pairs are mislabeled. In this paper, we propose an interpretable and learnable framework for risk analysis, which aims to rank the labeled pairs based on their risks of being mislabeled. We first describe how to automatically generate interpretable risk features, and then present a learnable risk model and its training technique. Finally, we empirically evaluate the performance of the proposed approach on real data. Our extensive experiments have shown that the learning risk model can identify the mislabeled pairs with considerably higher accuracy than the existing alternatives.

/pdf/towards-interpretable-and-learnable-risk-analysis-for-entity-2p6fwn8hho.pdf

Towards Interpretable and Learnable Risk Analysis for Entity Resolution

The state-of-the-art techniques for aspect-level sentiment analysis focused on feature modeling using a variety of deep neural networks (DNN). Unfortunately, their performance may still fall short of expectation in real scenarios due to the semantic complexity of natural languages. Motivated by the observation that many linguistic hints (e.g., sentiment words and shift words) are reliable polarity indicators, we propose a joint framework, SenHint, which can seamlessly integrate the output of deep neural networks and the implications of linguistic hints in a unified model based on Markov logic network (MLN). SenHint leverages the linguistic hints for multiple purposes: (1) to identify the easy instances, whose polarities can be automatically determined by the machine with high accuracy; (2) to capture the influence of sentiment words on aspect polarities; (2) to capture the implicit relations between aspect polarities. We present the required techniques for extracting linguistic hints, encoding their implications as well as the output of DNN into the unified model, and joint inference. Finally, we have empirically evaluated the performance of SenHint on both English and Chinese benchmark datasets. Our extensive experiments have shown that compared to the state-of-the-art DNN techniques, SenHint can effectively improve polarity detection accuracy by considerable margins.

Zhanhuai Li

Papers

Constructing domain-dependent sentiment dictionary for sentiment analysis

Aspect-level sentiment analysis based on gradual machine learning

Parallelizing maximal clique and k-plex enumeration over graph data

Towards Interpretable and Learnable Risk Analysis for Entity Resolution

Joint Inference for Aspect-Level Sentiment Analysis by Deep Neural Networks and Linguistic Hints