Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification

Convolution kernels support the modeling of complex syntactic information in machine-learning tasks. However, such models are highly sensitive to the type and size of syntactic structure used. It is therefore an important challenge to automatically identify high impact sub-structures relevant to a given task. In this paper we present a systematic study investigating (combinations of) sequence and convolution kernels using different types of substructures in document-level sentiment classification. We show that minimal sub-structures extracted from constituency and dependency trees guided by a polarity lexicon show 1.45 point absolute improvement in accuracy over a bag-of-words classifier on a widely used sentiment corpus

Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster

Modelling input texts: from Tree Kernels to Deep Learning

One of the core questions when designing modern Natural Language Processing (NLP) systems is how to model input textual data such that the learning algorithm is provided with enough information to estimate accurate decision functions. The mainstream approach is to represent input objects as feature vectors where each value encodes some of their aspects, e.g., syntax, semantics, etc. Feature-based methods have demonstrated state-of-the-art results on various NLP tasks. However, designing good features is a highly empirical-driven process, it greatly depends on a task requiring a significant amount of domain expertise. Moreover, extracting features for complex NLP tasks often requires expensive pre-processing steps running a large number of linguistic tools while relying on external knowledge sources that are often not available or hard to get. Hence, this process is not cheap and often constitutes one of the major challenges when attempting a new task or adapting to a different language or domain. The problem of modelling input objects is even more acute in cases when the input examples are not just single objects but pairs of objects, such as in various learning to rank problems in Information Retrieval and Natural Language processing. An alternative to feature-based methods is using kernels which are essentially non-linear functions mapping input examples into some high dimensional space thus allowing for learning decision functions with higher discriminative power. Kernels implicitly generate a very large number of features computing similarity between input examples in that implicit space. A well-designed kernel function can greatly reduce the effort to design a large set of manually designed features often leading to superior results. However, in the recent years, the use of kernel methods in NLP has been greatly under-estimated primarily due to the following reasons: (i) learning with kernels is slow as it requires to carry out optimization in the dual space leading to quadratic complexity; (ii) applying kernels to the input objects encoded with vanilla structures, e.g., generated by syntactic parsers, often yields minor improvements over carefully designed feature-based methods. In this thesis, we adopt the kernel learning approach for solving complex NLP tasks and primarily focus on solutions to the aforementioned problems posed by the use of kernels. In particular, we design novel learning algorithms for training Support Vector Machines with structural kernels, e.g., tree kernels, considerably speeding up the training over the conventional SVM training methods. We show that using the training algorithms developed in this thesis allows for training tree kernel models on large-scale datasets containing millions of instances, which was not possible before. Next, we focus on the problem of designing input structures that are fed to tree kernel functions to automatically generate a large set of tree-fragment features. We demonstrate that previously used plain structures generated by syntactic parsers, e.g., syntactic or dependency trees, are often a poor choice thus compromising the expressivity offered by a tree kernel learning framework. We propose several effective design patterns of the input tree structures for various NLP tasks ranging from sentiment analysis to answer passage reranking. The central idea is to inject additional semantic information relevant for the task directly into the tree nodes and let the expressive kernels generate rich feature spaces. For the opinion mining tasks, the additional semantic information injected into tree nodes can be word polarity labels, while for more complex tasks of modelling text pairs the relational information about overlapping words in a pair appears to significantly improve the accuracy of the resulting models. Finally, we observe that both feature-based and kernel methods typically treat words as atomic units where matching different yet semantically similar words is problematic. Conversely, the idea of distributional approaches to model words as vectors is much more effective in establishing a semantic match between words and phrases. While tree kernel functions do allow for a more flexible matching between phrases and sentences through matching their syntactic contexts, their representation can not be tuned on the training set as it is possible with distributional approaches. Recently, deep learning approaches have been applied to generalize the distributional word matching problem to matching sentences taking it one step further by learning the optimal sentence representations for a given task. Deep neural networks have already claimed state-of-the-art performance in many computer vision, speech recognition, and natural language tasks. Following this trend, this thesis also explores the virtue of deep learning architectures for modelling input texts and text pairs where we build on some of the ideas to model input objects proposed within the tree kernel learning framework. In particular, we explore the idea of relational linking (proposed in the preceding chapters to encode text pairs using linguistic tree structures) to design a state-of-the-art deep learning architecture for modelling text pairs. We compare the proposed deep learning models that require even less manual intervention in the feature design process then previously described tree kernel methods that already offer a very good trade-off between the feature-engineering effort and the expressivity of the resulting representation. Our deep learning models demonstrate the state-of-the-art performance on a recent benchmark for Twitter Sentiment Analysis, Answer Sentence Selection and Microblog retrieval

Nowcasting user behaviour with social media and smart devices on a longitudinal basis: from macro- to micro-level modelling

The adoption of social media and smart devices by millions of users worldwide over the last decade has resulted in an unprecedented opportunity for NLP and social sciences. Users publish their thoughts and opinions on everyday issues through social media platforms, while they record their digital traces through their smart devices. Mining these rich resources offers new opportunities in sensing real-world events and indices (e.g., political preference, mental health indices) in a longitudinal fashion, either at the macro (population)-, or at the micro(user)-level. The current project aims at developing approaches to “nowcast" (predict the current state of) such indices at both levels of granularity. First, we build natural language resources for the static tasks of sentiment analysis, emotion disclosure and sarcasm detection over user-generated content. These are important for opinion monitoring on a large scale. Second, we propose a general approach that leverages textual data derived from generic social media streams to nowcast political indices at the macro-level. Third, we leverage temporally sensitive and asynchronous information to nowcast the political stance of social media users, at the micro-level using multiple kernel learning. We then focus further on the micro-level modelling, to account for heterogeneous data sources, such as information derived from users' smart phones, SMS and social media messages, to nowcast time-varying mental health indices of a small cohort of users on a longitudinal basis. Finally, we present the challenges faced when applying such micro-level approaches in a real-world setting and propose directions for future research

Information overload in structured data

Information overload refers to the difficulty of making decisions caused by too much information. In this dissertation, we address information overload problem in two separate structured domains, namely, graphs and text. Graph kernels have been proposed as an efficient and theoretically sound approach to compute graph similarity. They decompose graphs into certain sub-structures, such as subtrees, or subgraphs. However, existing graph kernels suffer from a few drawbacks. First, the dimension of the feature space associated with the kernel often grows exponentially as the complexity of sub-structures increase. One immediate consequence of this behavior is that small, non-informative, sub-structures occur more frequently and cause information overload. Second, as the number of features increase, we encounter sparsity: only a few informative sub-structures will co-occur in multiple graphs. In the first part of this dissertation, we propose to tackle the above problems by exploiting the dependency relationship among sub-structures. First, we propose a novel framework that learns the latent representations of sub-structures by leveraging recent advancements in deep learning. Second, we propose a general smoothing framework that takes structural similarity into account, inspired by state-of-the-art smoothing techniques used in natural language processing. Both the proposed frameworks are applicable to popular graph kernel families, and achieve significant performance improvements over state-of-the-art graph kernels. In the second part of this dissertation, we tackle information overload in text. We first focus on a popular social news aggregation website, Reddit, and design a submodular recommender system that tailors a personalized frontpage for individual users. Second, we propose a novel submodular framework to summarize videos, where both transcript and comments are available. Third, we demonstrate how to apply filtering techniques to select a small subset of informative features from virtual machine logs in order to predict resource usage