Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things (IoT). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the word-level and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-the-art emotion prediction algorithms

MINING ACTIONABLE INTENTS IN QUERY ENTITIES

Understanding search engine users’ intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users’ future actions. In this paper, we present a novel research for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, i.e. the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment based on the Action Mining (AM) query entity dataset from Actionable Knowledge Graph (AKG) task at NTCIR-13 suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users

Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification

High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this paper, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions

Validity of Equation-of-Motion Approach to Kondo Problem in the Large-NN limit

The Anderson impurity model for Kondo problem is investigated for arbitrary orbit-spin degeneracy $N$ of the magnetic impurity by the equation of motion method (EOM). By employing a new decoupling scheme, a set self-consistent equations for the one-particle Green function are derived and numerically solved in the large-$N$ approximation. For the particle-hole symmetric Anderson model with finite Coulomb interaction $U$, we show that the Kondo resonance at the impurity site exists for all $N \geq 2$. The approach removes the pathology in the standard EOM for N=2, and has the same level of applicability as non-crossing approximation. For N=2, an exchange field splits the Kondo resonance into only two peaks, consist with the result from more rigorous numerical renormalization group (NRG) method. The temperature dependence of the Kondo resonance peak is also discussed.Comment: 4 pages, 2 eps figure

Comparison of a dyadic wavelet image enhancement algorithm with unsharp masking and median filtering for mammography

Image processing techniques using wavelet signal analysis techniques have shown promise in mammography. Wavelet algorithms are compared with traditional image enhancement techniques of unsharp masking and median filtering. Computer simulated phantom images were generated containing lesions mimicking masses and microcalcifications. The degree of image enhancement was evaluated by comparing processed and original signal-to-noise (SNR) ratios in such phantom images. Results obtained in this study suggest that image processing algorithms based on the wavelet transform are likely to enhance the visibility of low-contrast features in mammograms

Kondo Resonance in the Presence of Spin-Polarized Currents

We propose an improved method of the equation of motion approach to study the Kondo problem in spin-dependent non-equilibrium conditions. We find that the previously introduced additional renormalization for non-equilibrium Kondo effects is not required when we use a proper decoupling scheme. Our improved formulation is then applied to address the spin-split Kondo peaks when a spin current injects into a Kondo system.Comment: 4+ pages, 4 eps figure

Exploring Emotional Words for Chinese Document Chief Emotion Analysis

Abstract. In this study, we develop a document emotion analysis model by making use of the function of emotional words annotation. Eight basic emotion categories have been selected, including Expect, Joy, Love, Surprise, Anxiety, Sorrow, Anger and Hate, for both the word and the document level emotion analysis. We introduce two parameters, term relevance and term frequency, to evaluate the relations between the word emotions and the document emotions. Promising experimental results reveal the effectiveness of our document emotion analysis model under different emotion situations

Research on Soft Rock Damage Softening Model and Roadway Deformation and Failure Characteristics

To determine a reasonable control strategy for deep buried soft rock roadways, a study on deformation and failure characteristics was carried out. The Weibull distribution damage variable was introduced to construct a damage-softening model considering the lateral deformation of the rock mass, and the functional relationship between the model parameters F0 and m and the confining pressure were discussed. The nonlinear fitting method was used to correct the model parameters. Using the model, the failure characteristics of deep buried soft rock roadways were analyzed. A comprehensive and step-by-step joint support control strategy was proposed based on the numerical simulation results. The research results showed that the damage-softening model curve established could genuinely reflect the whole process of mudstone failure. The apparent stress concentration phenomenon occurred in the surrounding rock. The surrounding rock deformation showed that roadway floors had larger plastic failure areas than sides and vaults. The plastic failure depth could reach 2.45 m. After a comprehensive and step-by-step joint support control strategy was adopted, the deformation rate of the roadway at the section was less than 0.1 mm/d. The optimized support scheme can effectively improve the stability of the roadway

SsCak1 Regulates Growth and Pathogenicity in Sclerotinia sclerotiorum

Sclerotinia sclerotiorum is a devastating fungal pathogen that causes severe crop losses worldwide. It is of vital importance to understand its pathogenic mechanism for disease control. Through a forward genetic screen combined with next-generation sequencing, a putative protein kinase, SsCak1, was found to be involved in the growth and pathogenicity of S. sclerotiorum. Knockout and complementation experiments confirmed that deletions in SsCak1 caused defects in mycelium and sclerotia development, as well as appressoria formation and host penetration, leading to complete loss of virulence. These findings suggest that SsCak1 is essential for the growth, development, and pathogenicity of S. sclerotiorum. Therefore, SsCak1 could serve as a potential target for the control of S. sclerotiorum infection through host-induced gene silencing (HIGS), which could increase crop resistance to the pathogen.Science, Faculty ofNon UBCBotany, Department ofReviewedFacultyResearche