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With the development of deep learning, word vectors (i.e., word embeddings) have been extensively explored and applied to many Natural Language Processing tasks (e.g., parsing, Named Entity Recognition, etc). However, the semantic word vectors learned from context have insufficient sentiment information for performing sentiment analysis at different text levels. In this work, we present three Convolutional...
The problem of bridging the gap between image and natural language has gained more and more attention in recent years. This paper continues to push the study and improves the bidirectional retrieval performance across the modalities. Unlike previous works that target at single sentence densely describing the image objects, we extend the focus to associating deep image representations with noisy texts...
Previous work has shown that perceptual texture similarity and relative attributes cannot be well described by computational features. In this paper, we propose to predict human's visual perception of texture images by learning a non-linear mapping from computational feature space to perceptual space. Hand-crafted features and deep features, which were successfully applied in texture classification...
Divergent thinking refers to a style of thinking that ranges across a broad range of concepts, and is considered to be a core enabler of creativity. Thinking is often modeled as a process of conceptual combination, and creative ideas are seen as those using unconventional combinations of concepts. Since conceptual combination is fundamentally an associative process, it has been proposed that creative...
Sentiment classification has been a very hot topic in the field of natural language processing (NLP) and understanding in recent years. Recurrent neural networks (RNN) is a widely used tool to deal with the classification problem of variable-length sentences. The standard RNN can only access the preceding context of a sentence. In this paper, a new architecture termed Comprehensive Attention Recurrent...
Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live/dead assay dataset from the Broad Bioimage Benchmark Collection. These architectures include a standard convolutional network which produces single pixel...
Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label...
Microblog post has been a hot research source for emotion classification in recent years. However, due to bloggers' free narrative style and topics' timeliness, the data from microblog post is usually implicit and imbalanced. In this paper, the problems of emotion classification in Chinese microblog posts are solved in a hierarchical way using a knowledge-based topic model and Support Vector Machine(SVM)...
Biologically inspired episodic memory is able to store time sequential events, and to recall all of them from partial information. Because of the advantages of episodic memory, the biological concepts of episodic memory have been utilized to many applications. In this research, we propose a new memory model, called Deep ART (Adaptive Resonance Theory), to make a robust memory system for learning episodic...
Growing volumes of text and increasing expectations on the complexity of analysis entail advanced approaches to text mining. Unsupervised text clustering is an efficient approach to determine structural groupings in a text corpus without the impact of external bias. The information content of such structural groupings needs to be enhanced by integrating semantics into the cluster outcomes. This integration...
The classification of high dimensional data is an arduous task especially with the emergence of high quality data acquisition techniques. This problem is accentuated when the whole set of features is needed to learn a classifier such as the case of genomic data. The Bayesian approach is suitable for these applications because it represents graphically and statistically the dependencies between the...
Taking advantage of the large scale corpus on the web to effectively and efficiently mine the topics within texts is an essential problem in the era of big data. We focus on the problem of learning text topic embedding in an unsupervised manner, which enjoys the properties of efficiency and scalability. Text topic embedding represents words and documents in a semantic topic space, in which the words...
Short text is prevalent on the Web, but it brings challenges to content analysis methods for the lack of contextual information. Biterm topic model (BTM) is a variant of latent Dirichlet allocation, which effectively infers the latent topic distribution of short text by modeling the generation of biterms in the whole corpus. However, it needs fine-tuning from labels to reduce noise when applied to...
Community-based Question Answering (CQA) sites have become popular since they allow users to get answers to complex, detailed and personal question from other users directly. However, since answering a question depends on the ability and willingness of other users to address the askers' real needs, a significant fraction of the questions remain unanswered. To decrease the unanswered question rate...
A microblog recommendation method based on tag correlation and user social relation is proposed via analyzing microblog features and the deficiencies of existing microblog recommendation algorithm. Specifically, a tag retrieval strategy is established to add tags for unlabeled users and users with few tags, and the user-tag matrix is then built and user-tag weights are then obtained. In order to solve...
Hashing-based methods seek compact and efficient binary codes that preserve the similarity between data. For most existing hashing methods, an input (e.g. image) is first encoded as a vector of hand-crafted visual feature, followed by a hash projection and quantization step to obtain the compact binary vector. Most of hand-crafted features only encode low-level information of the input, the feature...
The recently introduced DeepIR model is proven effective for text classification [1]. In this paper, a modified DeepIR model is proposed by introducing a new document probability. This probability employs composite log likelihood method. An experiment using the modified DeepIR model is conducted on five text classification data sets. The proposed model shows considerable improvements in multi-class...
Semantic context is an important and useful cue for scene parsing in complicated natural images with a substantial amount of variations in objects and the environment. This paper proposes Spatially Constrained Location Prior (SCLP) for effective modelling of global and local semantic context in the scene in terms of inter-class spatial relationships. Unlike existing studies focusing on either relative...
Temporal alignment aligns two temporal sequences and is quite challenging due to drastic differences among temporal sequences and source data from different views. Canonical time warping (CTW) has shown great potential in temporal alignment tasks because it can reduce data redundancy by transforming high-dimensional data to a lower-dimensional subspace via canonical correlation analysis (CCA). However,...
The advent of the Social Web has provided netizens with new tools for creating and sharing, in a time- and cost-efficient way, their contents, ideas, and opinions with virtually the millions of people connected to the World Wide Web. This huge amount of information, however, is mainly unstructured as specifically produced for human consumption and, hence, it is not directly machine-processable. In...
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